Mosquito Species Detection using Smart Phone

Mosquito Species Detection using Smart Phone Abstract-According to WHO(World Health Organization) re-ports, among all disease transmitting insects mosquito is the most hazardous insect. In 2015 alone, 214 million cases of malaria were registered worldwide. Zika virus is another deadly disease transmitted from mosquitoes. According to CDC report, in 2016 62,500 suspected case of Zika were reported to the Puerto Rico Department of Health (PRDH) out of which 29,345 cases were found positive. There are 3500 different species of mosquitoes present in the world out of which 175 types is found in United States. But only few of them are responsible for these above mentioned fatal disease. Therefore classification between hazardous and regular mosquitoes are very important. For regular person with no expertise in this field would be almost impossible to identify the difference. Even for the mosquito-expert, identifying different species is a very tedious and time consuming job. Hence in this paper, we have tried to classify 7 different species of dead mosquitoes with total 60 samples collected from Hillsborough County Mosquito and Aquatic Weed Control Unit,Tampa Florida by capturing image from smart phone cameras. With our approach we want to enable non-expert population to early identify the risk and act pro-actively. We pre-processed the image for removing noise and applied random forest classification algorithm to distinguish different species. Achieved good precision,recall,F1 measure and aggregate 83:3% accuracy. We are also planning to develop a smart-phone application which will leverage this learning model and help in empowering population to identify mosquito species without any knowledge in this field. INTRODUCTION Of all animals, mosquitoes are amongst the most deadly in spreading diseases. Mosquito borne diseases like Malaria, Dengue, West Nile Fever, and most recently Zika Fever have extracted devastatic tolls on humanity [1]. Combating the spread of mosquitoes is an important health-care agenda across the globe, and several organizations across the globe serve this purpose. For instance, one such organization is the American Mosquito Control Association (AMCA) is spread over 50 countries and conducts numerous programs to educate citizens of the dangers posed by mosquitoes and how to control them. According to CDC report, there are about 3500 different species of mosquitoes in the world, out of which about 175 different species are found in the USA. Among programs designed to combat mosquitoes spread, identification of the type and number of species in any par-ticular area is very important. Across the world, numerous mosquito control organizations have dedicated personnel that lay traps to catch mosquitoes in specific areas, and dedicated personnel visually look at each captured sample (via a mag-nifying glass) to identify the type of mosquito. It takes upto a minute to identify each sample, and with more samples, the time taken to identify each sample can take hours, and naturally significant manual effort. Contributions of this Paper: In this paper, we aim to de-sign a system that combines images from smart-phone cameras with machine learning algorithms for automatic detection of the type of mosquito species from their images. Towards this extent, our specific contributions are: a). Building a database of mosquito images: We visited the Hillsborough County Mosquito and Aquatic Weed Control in Tampa in Fall 2016 to collect numerous samples of mosquitoes that were captured in traps set up the county personnel. Subsequently, the personnel helped us visually identify the type of each sample. As a result, we collected 60 samples, that belonged to seven different species.Table I presents our database. Subsequently, each sample was imaged via a Sam-sung Galaxy S5 phone via multiple angles (at the same indoor light conditions) for a total of 200 images. This served as our database for subsequent classification. b). Designing Pre-processing Techniques: Generally, images are vulnerable to the different type of noises due to different environment condition and user expertise. Therefore, images need to be pre-processed for any noise removal and also for smoothening. In the process of noise removal, we need to make sure that edges and boundary of images are preserved otherwise images will lose the key information. We used median filter as it works very effectively when edges need to be preserved. This filter is widely used in image processing technique [2]. c). Designing Random Forest Based Classifiers: Random Forest is an ensembled supervised machine learning algorithm. It is a collection of decision trees, where each trees has been grown using subset of training dataset selected randomly. In most of the cases, it has shown significant improvement in accuracy as compare to other classification algorithm. Apart from that, it works very well on outliers and noise. It handles larger dataset efficiently and quickly without over-fitting the model as only a subset of training set is selected for each split.[3] [4] We conducted an extensive performance evaluation for our proposed techniques.We evaluated our experiment on 60 image samples of seven different species. 10-fold cross validation technique has been used and achieved 83:3% accuracy using RGB features. The rest of the paper is organized as follows. In section II, related works are discussed. Followed by section III where experimental set up and data collection process are described. Section IV contains the detail about preprocessing of image data, extracting and selecting features, building the learning model using classification method and different metrics lever-aged for showing the results. We talked about experimental evaluation and validation in detail in section V. Finally, dis-cussion and conclusion sections are VI and VII respectively. RELATED WORK There are many studies which are dedicated to leverage the use of smart phone camera for image recognition. In this section we have emphasized few of the related and important works done. A. Related Work on Image Recognition In [5] system was developed for determining the effec-tiveness of soil treatment on plant stress using smart-phone cameras.In this paper, 34 images of plant leaves are captured using smart phone in two soils that is biosolids and unamended tailings. Then each images was preprocessed using mean, me-dian filter followed by segmentation into pixels.They extracted RGB,R,G,B,HSV and YCbCr features from the segmented pixels. Random Forest which is a supervised classification algorithm was designed to detect the stress of leaves and achieved 91.24% accuracy. A [6] survey has been done on Pixel-Based skin color detection techniques. They have applied various color spaces like RGB, Normalized RGB, HSV and YCrCb for recognizing skin. RGB is the most widely used color spaces for processing and storing digital images. Wen et.al [7] has proposed image-based automated insect iden-tification and classification method. In this paper eight insect species have been selected for experiment. These insects were frozen to retreive a non damaging kill of the insect and then they were placed on a white balance panel under the reflectance light base of a Nikon stereoscopic zoom microscope SMZ1000 (Nikon, Tokyo) with Plan Apochromat 0.5 objective. Images of these were taken by a DS-Fi1 color digital camera which was placed on the microscope. Features which had been taken in these are color, texture, invariants, contour and geometric. In color features, HSV color space features were considered. T Many classification algorithm i.e. minimum least square linear classifier (MLSLC), normal densities based linear classifier (NDLC), K nearest neighbor classifier (KNNC), nearest mean classifier (NMC), and decision tree (DT) were used for testing and training the model. Among these NDLC classification algorithm outperfor ms other classifier. 1) Comparing our Work w.r.t. Related Work: Our work is focused on capturing mosquitoes images from smart phone camera and using the captured image for training and testing the learning model. In [7], authors have identified insect species but it needs lab set up with microscope and high resolution digital camera which is not available in house generally. We have extracted RGB features for classification which is most widely used color spaces [6]. EXPERIMENTAL SETUP AND DATA COLLECTION In this section, we have discussed data collection process our experiment. A. Data Collection We collected dead mosquito species samples from Hillsbor-ough County Mosquito and Aquatic Weed Control Unit,Tampa Table I: Mosquito Species and Number of Samples Specie Name Number of Samples Cx Nigrip 10 An Quadrim 6 Ma Titillans 7 Ps Columpi 10 An Crucians 10 Ps Ferox 7 Cq Perturbans 10 Table II: Camera Specification Camera Specification Value Sensor Resolution 16 MP Focus Adjustment automatic Special Effect HDR Camera Light Source Daylight Florida. We carefully identified seven species, mentioned in Table I for our study. Since, dead mosquito physical properties like color, del-icateness etc changes as time passes. So, images of dead mosquitoes were taken in a single day to make sure envi-ronmental conditional are same while taking these images. A Samsung Galaxy S5 smartphone was used for capturing images in regular day light. Each sample image was taken based on the knowledge aware fusion described on the mosquito and aquatic control weed control unit web site. A total of 60 images were captured for our study, having following camera configuration, mentioned in Table II. OUR APPROACH We have implemented two steps in our approach. First, pre processing of image has been done for noise removal and feature selection using filter like median,mean. Second, building a learning model using a classification algorithm based on random forest. Here our main aim is to build a learning model for identi-fying each mosquitoes species. The challenge here we faced is the image size. Images which were captured from smart phone is of 2988 X 5322 pixels. We reduced their size to 256 X 256 pixels to decrease its data dimensionality. To remove the noise from each sample we applied median filter technique.This has been elaborated in the next subsection. Since, our images were already in dark color.It is mandatory to keep background and foreground in contrast for building the model reasonably well. So, we did not use any segmentation technique as it converts the background into black. Here,we are using Random Forest, a supervised learning algorithm and used 10-fold cross validation technique for learning and testing. The process flow of our algorithm is described in Figure 2. For proceeding further, we need labeled image data for training the model. All images were tagged manually under the guidance of mosquito experts. Noise Removal Generally, digital images are susceptible to different type of noise. It can occur by several ways like capture, transmission etc. Accuracy of the result are affected badly by the same. There are many filters used to remove and reduce noise from image. Sharpening Filter: It refers as a enhancing technique which highlights edges and line details in the image. In this procedure, original image is passed through high pass filter which extracts its high frequency components and then the scaled output of high pass filter is added to original image which results in sharpened image. [8] Mean Filter: This filtering technique refers to replacing each pixel value in an image with the mean of pixel values of its neighbors which falls in the sliding window of n*n size. This technique removes noise more effectively if large window size is considered.This is also called average filter. [8] Median Filter: It is a nonlinear filtering technique. The approach behind this filtering technique is to replace each pixel value in the window of n * n size pixel by the median of all pixel values in that particular window.It is very used in digital image processing and it preserves edges while removing noise. We have used this filtering technique with 3*3 pixels window size for removing the noise from our digital images. The output with median filter and without this is shown in Figure 1. [2] Feature Selection Feature extraction and selection is very critical part of any supervised learning algorithm. Extraction is about reducing the data dimensionality as the size of data grows and its dimension increases and becomes very difficult to handle it manually . And then the need of automation comes into the picture. Feature Selection is a process of selecting those features which are most relevant for our problem and eliminating unnecessary, irrelevant and redundant features of data that do not contribute to the accuracy of learning model. In our proposed model, we are identifying different species of mosquitoes. Each species have contrastive color. As we can see in Figure 3, each mosquitoes have similar shapes but differ-ent body and wings color.So,the correct color channels or the combination of channel is important to take into consideration for the features. Few of the color channels are RGB, HSV etc. RGB has Red, Green and Blue channels. In RGB, each component supports a range of intensity levels from 0 to 255 (integer valued)[9] . Here, we extracted RGB feature from the mosquito image data. Then for feature selection, we applied Information-Gain attribute selection algorithm which is a good measure for deciding the relevance of an attribute. This feature selection technique generally helps in achieving high accuracy and using this we got 1000 features which serve as an input vector x into Random Forest Classification Algorithm for species detection. We calculated its precision, recall and F1-measure which is mentioned in Table III Table III: Combination of color channels accuracy comparison Combination Precision Recall F1-measure RGB 0.845 0.833 0.834 C. Classification Method Random Forest Algorithm: Random Forests(RF) is an ensemble supervised machine learning algorithm. It consists of a set of decision trees; h(x,i) i = 1, 2,, where x is a feature vector extracted from the smartphone image data and i consists of K integers which are independent identically distributed random vectors. Each decision tree predicts a class independently. A voting is performed on the results from each decision tree and finally the class which gets majority vote will be the final predicted class. The same has been explained in Figure 4 . Given a dataset set that contains N feature vectors, each consisting of M features, the RF algorithm builds the trained model using following steps: N samples are selected at random with replacement from the data set, for training the model of a particular tree. K features are randomly selected from the set of available features, where K M. Among the values for each of the K features drawn, choose the best split according to the Information gain IG(T; a) of the attribute. Information gain is measure of decrease in entropy which is caused by splitting the samples on an attribute. T denote a set of training sample for a single tree. ((x),y) = (x1, x2,.., xk,y) where (x) consist is a single sample and y is its class label. The information gain for an attribute a is as follow: The information gain for an attribute a is as follows: IG (T; a) = H (T ) v val(a) j(x T jTa = v) j :H (x T jxa = v) X x j j (1) [10] Here, xa vals(a) is the value of the ath attribute of example x. The randomization is present in two ways: Random selection of data for bootstrap samples as it is done in bagging Random selection of input features for creating individual base decision trees. Each tree will grow to its maximum size until the stopping criterion has not been fulfilled and there will be no tree pruning. Once the forest has been ensembled, testing data sample will be labeled mosquito species class based on a majority vote among all classes from all decision trees in the forest. Once the forest has been ensembled, testing data sample is labeled with one of the classes (species1; species2::::species7) by taking the majority vote: i.e., it is labeled with the class which has been selected by maximum number of trees. In the RF approach, given a feature sample x to be classified, the conditional probabilities for each class are computed by taking the average of the conditional probabilities given by the trees constructing 4 Figure 1: a) Original Image b) Image after applying sharpening median filterFigure 2: Process description of our experiment a). Cruciansb). Columpic). Feroxd). Nigrip e). Peturbansf). Quadrimg). Titillans Figure 3: Mosquito Color Images the ensemble. These conditional probabilities are computed as follows. Given a decision tree T, and an input feature sample x to be classified, let us denote by v(x) the leaf node where x falls when it is classified by T. The probability P (mjx; T ) that the sample x belongs to the class m, where m 2 fspecies1; species2; :::; species7g (for 7 species of interest to this paper), is estimated by the following equation: P (mjx; T ) = nm (2) n where nm is the number of training samples falling into v(x) after learning and n is the total number of training samples assigned to v(x) by the training procedure. Given a forest consisting of L trees and an unknown feature sample x to be classified, the probability estimate P (mjx) that x belongs to the species m is computed as follows: 1 L (3) P (mjx) = P (mjx; Ti) L =1 Xi P (m x; T ) by where th j i is the conditional probability provided the i tree and is computed according to Eq.(1). As a consequence, for the sample x to be classified, the RF algorithm gives as output the vector: = fP (species1jx) ; P (species2jx) : : : : : : P (species7jx)g The class(species) with the highest probability in the set(4)is chosen as classified class for the ith tree. The final class of our RF algorithm is the one which gets the majority vote among all activities from all decision trees in the forest [11]. The work flow of the RF algorithm with pre-processing, training and testing phase is formally shown in Algorithm 1.[3] [12] D. Metrics The results of Mosquito-Species detection are shown in terms of precision, recall, F1-measure and Confusion Matrix. Each metric is a function of the of the true positives (T P ), false positives (F P ) and false negatives (F N). The precision is the ratio of correctly classified classes to the total number of classes predicted as positive: P recision = T P (5) T P + F P Recall is the ratio of total number of classes predicted as positive to the total number of positive classes: Recall = T P (6) T P + F N 5 Figure 4: Work flow of the Random Forest Algorithm The F1-measure is the weighted average of precision and recall: P recisionRecall F1 = 2 P recision + Recall (7) The Confusion Matrix (CM) is a table that allows the visu-alization used to describe the performance of a classification model. Each column of the matrix represents the instances in a predicted class while each row represents the instance in an actual class (or vice-versa) [13]. Precision indicates the number of samples classified as a particular species actually belonged to that species. Recall gives us the number of species which are correctly classified. The F1-measure denotes the classification models accuracy.It is calculated as the harmonic mean of precision and recall. Confusion matrix makes the system easy to see how much predicted model is getting confused between different species. For example if a species is predicted correctly only 80% of the time, then this matrix will show how the algorithm confused its prediction with the other (wrongly classified) species the remaining 20% of the time. RESULTS Overview of Evaluation Methods: In this paper, we evaluated the performance of our system using 10-fold cross validation that are standard for our problem scope. Cross-validation is a model validation technique for assess-ing how the results of a classification model will generalize to an independent dataset 10-fold cross-validation divides the dataset into 10 subsets, and evaluates them 10 times. Each time, one of the 10 subsets is used as the test set and the other 9 subsets are put together to form a training set. Then, the average error across all 10 trials is computed for final result. It limits problems like over-fitting in the classification model. Results and Interpretations: We used RGB feature men-tioned earlier to train our classification model. To evaluate its accuracy we used 10-fold cross validation technique and calculated precision, recall and F1 measure of each species independently. The evaluation measures of RGB feature are shown in IV have also shown it graphically in Figure 5. Confusion Matrix of the same is shown in Figure 6. Algorithm 1: RF-based Algorithm for Mosquito-Species detection Training Image dataset = Id; Testing Image dataset= Ited; RGB Features extracted from Training Image dataset = F tRGB; RGB Features extracted from Testing Image dataset = F teRGB; Classified Species from Images= M S; Probability that feature F belongs to Species M S = P (M SjF ); No. of trees in Random Forest = 121; Step 1 Pre-Processing: Median filters are applied to remove accidental spikes from Id and Ited. Features F tRGB and F teRGB are extracted from processed data Id and Ited obtained from (1). Step 2 Training: Input: Training data set F tRGB Output: Random Forest model to classify different species of mosquitoes. Select a bootstrap sample of size N from the training data. Grow a decision tree T using following steps. Select K features at random from the set of M features. Choose the best feature/split-point among the K. Split the node into two daughter nodes. Grow the tree to its maximum size that is 6 and let the tree unpruned Step 3 Prediction: Input: Testing data set Ited Output: Final Mosquito Species prediction M Ss. Select the same attributes used for training the model from testing feature set F teRGB. Predict the species from the model using features selected in the above step. 6 Table IV: RGB Features accuracy of each species indepen-dently Species Precision Recall F1-measure An Crucians 0.889 0.8 0.842 An Quadrim 0.571 0.667 0.615 Cd Peturbans 0.727 0.8 0.762 Cx Nigrip 0.889 0.8 0.842 Ma Titillans

“Lord of the Flies”: a Microcosm to Our Society Essay

William Golding s novel Lord of the Flies significantly symbolizes characters, objects and the setting to represent our world as a whole. Golding uses those symbols to make the island similar to society and to show the difference between living in a civilised society and savagery. The novel takes place on an island during World War II, this is significant since the isolation forms a sort of civilization and community, a sort of microcosm to the real world and to human civilization. Lord of the Flies island is similar to our society in many ways. There are certain objects that link the island and our society. Firstly William Golding portrays the conch as a symbol for power, order and authority. This is because whoever holds it becomes the only one with the authority to speak. That rule is official when Ralph says, And another thing. We cant have everybody talking at once. Well have to have Hands up like at schoolthen Ill give him the conch. (Golding 31) This is much like standing at the podium or holding a microphone in our civilized society or raising ones hand in school. The conch also represents the organization found in a democratic Government. Since the conch was discovered it has created order and rules and brought the boys together as a society. Jack says well have rules! he cried excitedly. Lots of rules! Then anyone who breaks em- (Golding 32) As the conch starts to lose its power the tribe splits into two groups, just as the community would have if the government would no longer have control. The rise and fall of the conch symbolized the rise and fall of a system of rules, regulations and order in a society. It provided a parallel universe showing how important the rules and regulations are to a successful society. Therefore the conch is a symbol of human civilization thus making Lord of the Flies a smaller version of society. Another symbol of civilization are the glasses belonging to piggy. It symbolized knowledge and insight. They represent societys reliance upon technology and the weakness of human beings. They were created to make piggy s life better, without them he can hardly see. When Jack snatches the glasses off piggy s face, Piggy replies Mind out! Give em back! I can hardly see! (Golding 40) The glasses are symbolic to technology which makes ones life more convenient or improves their quality of life. Without the glasses  the boys would have not been able to start a fire since no one else knew another way to do so. After asking around for matches Jack points suddenly at Piggy and says His specs-use them as burning glasses! (Golding 40) The concept that the boys would be helpless without the glasses symbolizes how helpless people become when they dont have technology to aid them . Also the clothes are a symbolic link to the survival of civilization and a symbol of conflict in society. In our society today, the clothing someone wears tells a lot about them. People are judged to be rich or poor, intelligent or unintelligent, or even neat or messy just by the clothing they have on. The boys arrive with clothes, but gradually they all wear less and less. This is symbolic of their becoming more savage and less in touch with their former civilization. One who does not wear pants in our society would be arrested and considered to be uncivilized. Another use of symbolism in clothing is that Jack and his group all dress alike and act alike makes them into a separate group from the rest of the boys. They carry out different jobs and have a different ideology. This conflict of a separate group in the society which Ralph trys to organize eventually leads to the break up of the boys into completely separate groups. This is symbolic of the different cultures we have in our society today. As the novel is an allegory, each character signifies an important idea or theme. Each character is symbolized clearly as someone in our society. Piggy is the scientific and intellectual aspect of society. He is a thinker, philosopher and someone who always advises for good. He was also like Albert Einstein, people did not care that he thought the atomic bomb was a bad weapon just like no one cared about what piggy said or did. While Piggy was trying to explain himself to Ralph, Jack turned on him. You shut up!, he yelled. (Golding 46) Another main character Ralph is like Franklin Roosevelt , who could not stop the World war two from breaking out. He is the allegory of the confused that are always confused in the recognition between good and evil. He also represents democracy and is responsible. He is the politician who relies on social order and government, and his political failures show that he cannot control the evil within the other boys. Jack was a symbol of Adolph Hitler. He w as a crazy leader who killed many people because he wanted power, No one moved. Jack shouted angrily. I said grab them! (Golding  198) Like Hitler, Jack was a dictator and ignorant. Jack is the hunter, protector and the Soldier who is consumed by his own fear and the greater force of his own capacity for evil. Roger is even worse than Jack, even though he isnt like that from the beginning. Hes similar to Satan or even Dr. Mengele who was Hitler s worker and did very bad operations on people. Roger is so cruel and has gone far beyond from being a savage when Roger purposely with a sense of delirious abandonment, leaned all his weight on the lever, resulting in Piggy s death. (Golding 200) Simon is a Christ like figure who discovered the truth but unlike Christ he is sacrificed before he can deliver it to the others. The impulses and the behaviour of the boys are those of adults. The struggle between Ralph and Jack is symbolic of the struggle between the forces of civilization and anarchy, or the struggle between moral conscience and his heart of darkness and also world war 2. The tussle between Jack and Ralph for leadership is the allegory of our political leaders who always fight and kill for the sake of power. The way the boys are being seduced by Jack, the leaders of our world exploit the masses. Our world is very fertile like the island, but in their ambition to get the power the leaders destroy this world just like the boys who burn the whole island to ashes without realizing that they are destroying their own means of survival. The division of the boys in litluns and big’uns is the allegory of the classes in our world. Litluns symbolize the common people, while the big’uns are the allegory of the ruling, powerful and political classes. The island on which the boys find themselves is allegorically our world in miniature. Since the island is a microcosm, Golding uses it to reflect our world and give comments on it and his view of human nature. In order to achieve this symbolism, Golding uses objects that have symbolic value as references to ideas, characters that symbolize important historical and religious people, and the setting which frames the conflicts on the island, all have parallels in the real world. They show that what happened in the story is just not a story. They help prove the authors belief that humankind is savage and evil is under civilization. Thus the Lord of the Flies is a microcosm, a smaller version of society and human civilization. BIBLEOGRAPHY: Lord of the Flies, William Golding

Appendicitis Is The Inflammation Of The Appendix, Which

Appendicitis is the inflammation of the appendix, which causes the individual to be in a lot of pain and discomfort. It’s a very common case that is seen in the emergency department and any age group is susceptible to suffer from the infection. This essay will firstly give a detailed explanation on the pathiophysiology of appendicitis, secondly it will cover the theories of growth and development of a 10-year-old adolescent, the third topic covered will be family centred care. Lastly covered will be the affects hospitalisation has on the 10-year-old adolescent and their family. This paper will be focused on a casey study, a 10-year-old adolescent experiencing appendicitis whilst being supported by her parents. Appendicitis is an†¦show more content†¦(Medscapecom, 2017). Anne is to undergo a appendectomy which is a small operation to remove the inflamed appendix. The process used to remove the organ is called a Laparoscopic appendectomy which isn t invasive to the client. The surgeons remove the appendix from a single incision near the umbilicus, this process is used to prevent minimal scarring. (YongHun, K., WooSurng, L. (2016). During Anne’s operation the medical surgical team discover that her appendix has perforated and has caused a gangrenous infection, peritonitis. Peritonitis occurs when the appendix ruptures and bacteria enters the gastrointestinal tract. If this bacterial infection is left untreated it can spread and cause other serious health problems, such as a blood stream infection. Treatment for peritonitis will be a round of antibiotics, the type of antibiotic will be determined by how extensive the infection is. The antibiotics will boost the fight of infec tion and prevent the spread to surrounding organs. (Mayoclinicorg, 2017). As with everything, there is always psychology behind something. One of the most important growth and development theories is Jean Piaget’s cognitive development theory. In 1963 Piaget’s constructed a detailed and educational theory which solely focused on how a child mentally constructs a model of the world they live in. Piaget’s systematic cognitive development study includes stages of cognitive development, observationalShow MoreRelatedThe Appendix Is A Safe Haven For The Normal Flora Of The Gut Essay947 Words   |  4 PagesPatho The appendix is a small finger like attachment hooked to the cecum of the large intestine in the lower right portion of the abdomen. It has long been regarded as useless in today’s population, a â€Å"hanger on,† from a time of the past when it served some unknown function. Due to a lack of symptoms after its removal, that theory has been generally accepted. There are, however, theories developing that the appendix is used as a safe haven for the normal flora of the gut. These theories claimRead MoreFamily Centered Care : A Collaboration Among Patients, Families, Physicians, Nurses, And Other Professionals1176 Words   |  5 Pagesprovider more anxious (Harrison, 2009)† Pediatric disease process description and treatment Appendicitis is an inflammation of the appendix. The appendix is a 3 1/2-inch-long tube of tissue that extends from the large intestine, which is located in the lower right quadrant of the abdomen. Appendicitis is a medical emergency and requires surgery to remove the appendix. If left untreated, an inflamed appendix will burst, or perforate, â€Å"leaking out a thick liquid containing bacteria, tissue cells, andRead MoreAppendicitis Case Study1950 Words   |  8 Pagesvermiform appendix. This procedure is normally performed as an emergency procedure, when the patient is suffering from acute appendicitis. In the absence of surgical facilities, intravenous antibiotics are used to delay or avoid the onset of sepsis; it is now recognized that many cases will resolve when treated non-operatively. In some cases the appendicitis resolves completely; more often, an inflammatory mass forms around the appendix. This is a relative contraindication to surgery. APPENDICITIS AppendicitisRead MoreTime Goes By So Fast1415 Words   |  6 Pagesbrother in law never had complained about appendicitis so we never thought that this could be the case. We were wrong. What really got me unprepared was the fact that the same night he had to prepare for surgery. As I was alone at the waiting room of the hospital I started to do some research about appendicitis bothered by the fact that I couldn’t recognize it from my brothers in law symptoms. To be sincere I never thought about it. What is the appendix? The appendix is a pouch the size of a finger partRead MoreAcute Pain : Acute Abdominal Pain Essay2802 Words   |  12 PagesAppendicitis Appendicitis is an infection that requires immediate attention due to its severe harmful effects. Definition Appendicitis is the inflammation of the appendix. It is detrimental to treat the infection as soon as a diagnosis is confirmed. This usually causes the patient severe abdominal pain. In support the book states, â€Å"Appendicitis, inflammation of the vermiform appendix, is a common cause of acute abdominal pain.† The patient should be immediately treated due to the excessivelyRead MoreBenjamin Engelhart Essay2246 Words   |  9 PagesEMERGENCY SERVICES ADMISSION REPORT Patient Name: Benjamin Engelhart Patient ID: 112592 DOB 10/5/---- AGE: 46 SEX: Male Date of Admission 11/14/---- Emergency Room Physician: Alex McClure, MD Admitting Diagnosis: Acute Appendicitis HISTORY OF PRESENT ILLNESS: This 46-year old gentleman with past medical history significant only for degenerative disease of the bilateral hips, secondary to arthritis presents to the emergency room after having had 3 days of abdominal pain. It initiallyRead MoreThe Pathophysiology Of Mark s Presenting Compliant1628 Words   |  7 PagesAt the age of 8, a child often experiences a shift from the pre-operational thought when they were pre-schoolers to concrete operational thought. Mark who is 8 years old was undergoes an appendectomy and been diagnosed with a gangrenous perforated appendix with peritonitis. This essay will be describing the pathophysiology of Mark’s presenting compliant and also evaluating on the nursing role to deliver developmentally appropriate nursing care with the focus on Mark’s growth and developmental stageRead MoreClinical And Holistic Personal Health Factors Within An Emergency Nursing Framework3362 Words   |  14 Pageswoman with acute a bdominal pain requiring emergency assessment, and the subsequent diagnosis of acute appendicitis leading to surgical intervention. The aim of this paper is to explore the patient’s relevant presentation, clinical and holistic personal health factors within an emergency nursing framework. Two key areas of care will be identified to critique and examine in detail: acute appendicitis as an immediate concern, and long-term use of non-steroidal anti-inflammatory drugs (NSAIDs) and proton-pumpRead MoreHillcrest Medical Case 21899 Words   |  8 Pageson the study appeared thickened. Dilated appendix seen constant with acute appendicitis. Osseous structures of the abdomen appeared unremarkable. No free air was seen. PELVIS: Good quality, non contrasted actual CT examination of the pelvis with coronal reconstructions. Prostate, seminal vesicles and urinary bladder appeared WNL. The bowels seen on the study appeared WNL, except for inflammatory changes of the appendix and seccum with acute appendicitis. Osseous structures of the pelvis appearedRead MoreSymptoms And Treatment Of An Abdominal X Ray Essay1997 Words   |  8 Pagesacute appendicitis. She appears to be in a lot of pain, is on oxygen and has been vomiting. She speaks English and is accompanied by a nurse, her husband and their 2 year old child. 2.ACUTE APPENDICITIS The appendix is in the form of a pouchlike shape situated in the right lower quadrant of the abdomen. According to Health grades (2016), â€Å"Acute appendicitis is a rapidly progressing inflammation of a small part of the large intestine called the appendix.† The appendix in acute appendicitis, swells

Prostate Cancer The Most Common Cancer - 950 Words

Prostate cancer is the most common cancer in American men aside from skin cancer. Out of six American men at least one of them will be diagnosed with colon cancer. The prostate is a small gland shaped like a walnut that is in the center of a man s body within the pelvis. The prostate makes a milky fluid that carries sperm during ejaculation. It is wrapped around the tube that carries urine out of the body (the urethra). It sits just below the bladder. Prostate cancer is more common in African American men, than in men of other racial backgrounds. African American men are more often diagnosed with prostate cancer when it is in advanced stages. They are over 2 times more likely to die from prostate cancer than white men. Hispanic/Latino and Asian-American men are less likely to develop prostate cancer. A man’s age is what will put him at higher risk for prostate cancer. Men who are at higher risk are men between ages 40 and 50 years old. It is very rare for a man to be diagnosed with prostate cancer but as it is not common to be diagnosed with prostate cancer it is more likely to be diagnosed with it after the age of 50. Let it be known that more than 220,000 men will be diagnosed this year with prostate cancer, and over 20,000 men will lose their life from it. These statistics have been increasing over the years because there is no answer in what causes prostate cancer. It has been proven that prostate cancer does run in families and that it can be somewhat hereditaryShow MoreRelatedProstate Cancer : The Most Common Cancer2250 Words   |  9 PagesProstate cancer is the second most common cancer and it is the second leading cause of cancer death in American men according to the American Cancer Society(ACS) ( 2016). Older age is the strongest risk factor for the development of prostate cancer. Approximately 1 in 7 men will be diagnosed with prostate cancer during his lifetime (ACS, 2016). There are more than 2.9 million prostate cancer survivors in the U nited States (ACS, 2016). The risk of dying from prostate cancer is 2.9 percent, with seventyRead MoreProstate Cancer : The Most Common Types Of Cancer1576 Words   |  7 PagesIntroduction Prostate cancer is one of the most common types of cancer in men. According to the American Cancer Society, â€Å"there are nearly 3 million prostate cancer survivors in the United States today and this number is expected to increase to almost 4.2 million by 2024. Most prostate cancers (93%) are diagnosed at the local or regional stage, before the cancer has spread to surrounding organs. Treatment at these early stages is often very successful, and the 5-year relative survival rate approachesRead MoreProstate Cancer : The Second Most Common Type Of Cancer1510 Words   |  7 PagesProstate cancer is the second most common type of cancer diagnosed in men around the world today. Despite years of research, little is known as to the exact cause of prostate cancer, making it an area of intense research in medicine today. The pathology of prostate cancer has yielded important information on prevention, diagnosis and treatment methods. It has been understood that diet has much to do with tumour growth, and new research into nutrition is revealing new strategies in prostate cancerRead MoreDetecting Prostate Cancer : Cancer And Second Most Common Cancer Death Among Men2187 Words   |  9 PagesDetecting Prostate Cancer According to Ferrante, Shaw, and Scott (2011), prostate cancer is the most common cancer and second most common cancer death among men in the United States. Early detection permits appropriate and timely management, which can allow clinicians to treat the cancer effectively. When detected at early or regional stage, prostate cancer has a five-year survival rate of about 100%. Prostate-specific antigen (PSA) is the most widely used tumor marker and was approved by the FDARead MoreProstate Cancer Informative speech Essay example767 Words   |  4 PagesWhat is the Prostate? The prostate is a gland in the male reproductive system, which makes and stores a fluid that nourishes sperm. The prostate gland is about the size of a walnut, and surrounds the upper part of the urethra, the tube that empties urine from the bladder. If the prostate gland grows too large, the flow of urine can be slowed or stopped. Prostate Cancer Prostate cancer is the most common type of cancer in men in the United States (other than skin cancer), and accounts for moreRead MoreEssay about Prostate Cancer1002 Words   |  5 PagesProstate Cancer Eng 101 Andrell Nash Fortis College Abstract Prostate Cancer is a disease in which there is an uncontrolled growth of cells that lines the ducts of the prostate gland. The abnormal cells can spread throughout the prostate and nearby organs, such as the seminal vesicles. If not caught early they can spread (metastasized) to other organs of the body through the lymph or blood stream. (â€Å"...Treatment...† 13) Prostate Cancer can be like a silent killer. Some may show signs and symptomsRead MoreProstate Cancer : The Diagnosis Process, And Possible Treatment Options For The Disease1714 Words   |  7 Pagescarcinoma, or prostate cancer, in their lifetime (American Cancer Society, 2015). This scary statistic proves just how serious prostate cancer is. According to the American Cancer Society, an estimated 180,000 cases of prostate cancer are diagnosed in the United States. Of the 180,000, 32,000 result in death from the disease each year (American Cancer Society, 2015). This paper will explore what prostate cancer is, the diagnosis process, and possible treatment options for the disease. Cancer of theRead MoreRenal Cell Carcinoma Of The Kidney Cancer Essay1470 Words   |  6 PagesThe most common genitourinary cancers include, renal cell carcinoma of the kidney, transitional cell carcinoma of the bladder, and adenocarcinoma of the prostate. 1- Renal cell carcinoma, is the most common cancer of the kidney, accounts for approximately 90% of all renal malignancies. About 65,000 cases of renal cell carcinoma are diagnosed each year in the U.S. Despite advances in diagnosis, especially improved imaging techniques and the incidental diagnosis of many tumors with imaging tests forRead MoreEssay about Prostate Cancer1132 Words   |  5 PagesProstate Cancer Cancer of the prostate, a common form of cancer, is a disease in which cancer (malignant) cells are found in the prostate. The prostate is on the male sex glands, and is located just below the bladder and in front of the rectum. The size of the prostate is about the size of a walnut. It surrounds the part of the urethra, the tube that carries urine from the bladder to the outside of the body. The prostate makes fluid that becomes part of the semen, which contains sperm. ProstateRead MoreProstate Gland And Where It s Located1352 Words   |  6 PagesWhat is the prostate gland and where it’s located The prostate gland is about the size of a whole-nut in most men, it consists of 70% glandular tissue and 30% muscular tissue [2]. [Figure 1] shows that the prostate sits at the bottom of the pelvis right under the bladder, the floor of the bladder is basically the top of the prostate (it adheres firmly to the base of the bladder). The rectum wraps around the back of the prostate so the front wall of the rectum is lying over the prostate itself. The