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Chronic lymphocytic leukemia (CLL) is the most common type of blood cancer in Canadian adults. CLL cells are abnormal lymphocytes, which tend to be slightly larger than normal resting lymphocytes and have a condensed appearance to their chromatin. There is a low number of related works on this disease. This paper presents a method to segment normal and CLL lymphocytes into two parts: nucleus, and...
We describe the use of a genetic programming system to induce classifiers that can discriminate between Parkinson's disease patients and healthy age-matched controls. The best evolved classifer achieved an AUC of 0.92, which is comparable with clinical diagnosis rates. Compared to previous studies of this nature, we used a relatively large sample of 49 PD patients and 41 controls, allowing us to better...
In this paper, a novel method for detecting the onset of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is presented. It uses a combination of three different machine learning algorithms in order to get improved results and is based on a three-class classification problem. The three classes for classification considered in this study are normal, very mild AD and mild and moderate...
Feature selection is a vital process in classification of medical datasets. This paper addresses feature selection in Radial Basis Function (RBF) kernel space for the classification of multiclass dermatology dataset using neural network and data mining classifiers. It has three stages in determining relevant and irrelevant features for the classification task. In stage I, the features of dermatology...
We address the problem of weakly supervised segmentation (WSS) of medical images which is more challenging and has potentially greater applications in the medical imaging community. Training images are labeled only by the classes they contain, and not by the pixel labels. We make use of the Multi Image Model (MIM) for weakly supervised segmentation which exploits superpixel features and assigns labels...
This paper mainly discussed the process to classify Anthracnose and Downey Mildew, watermelon leaf diseases using neural network analysis. A few of infected leaf samples were collected and they were captured using a digital camera with specific calibration procedure under controlled environment. The classification on the watermelon's leaf diseases is based on color feature extraction from RGB color...
Accurate and real-time tracing of epidemic sources is critical for epidemic origin analyses and control when outbreaks of epidemic diseases occur. Such tracing requires the simultaneous availability of information about social interactions among people as well as their body vital signs. Existing epidemic control methods are limited due to their inability to collect the above two types of information...
Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases...
In the last two decades, several chaos-based cryptosystems have been proposed. Some of them have architecture comprising a layer of permutation and a layer of diffusion and these layers are simultaneously executed in a simple scan of plain-image pixels. In this kind of cryptosystems, due to the channel effect, a bit error(s) in the cipher-image produces, at the decryption side, a random bit error...
Truly, heart is successor to the brain in being the most significant vital organ in the body of a human. Heart, being a magnificent pump, has his performance orchestrated via a group of valves and highly sophisticated neural control. While the kinetics of the heart is accompanied by sound production, sound waves produced, by the heart, are reliable diagnostic tools to check heart activity. Chronologically,...
Listening to the heart sounds is a common practice in identifying cardiac malfunctions. Since this method has many limitations, tools that aid physicians in their diagnosis of heart diseases are very useful. This paper presents a software tool to predict cardiac abnormalities which can be identified using heart sounds. Both heart sound information and symptoms are used in disease prediction. First...
One of the main causes of death the world over are cardiovascular diseases, of which coronary artery disease (CAD) is a major type. This disease occurs when the diameter narrowing of one of the left anterior descending, left circumflex, or right coronary arteries is equal to or greater than 50 percent. Angiography is the principal diagnostic modality for the stenos is of heart vessels, however, because...
A model to predict the Length of Stay (LOS) for hospitalized patients can be an effective tool for healthcare providers. Such a model will enable early interventions to prevent complications and prolonged LOS and also enable more efficient utilization of manpower and facilities in hospitals. In this paper, we propose an approach for Predicting Hospital Length of Stay (PHLOS) using a multi-tiered data...
Needle Electromyography, in combination with nerve conduction studies, is the gold standard methodology for assessing the neurophysiologic effects of neuromuscular diseases. Muscle categorization is typically based on visual and auditory assessment of the morphology and activation patterns of its constituent motor units. A procedure which is highly dependent on the skills and level of experience of...
Parkinson's disease (PD) is a neurodegenerative disorder. With progression of PD, movement disorder such as gait disturbance and balance impairment is frequently observed. Hoehn and Yahr scale (HY) is an indicator to evaluate the severity of motor signs of PD. Recently, objective measurement comes to be widely spread. Previous studies pointed out that human walking comes from complex interaction,...
Several machine learning techniques have been applied for finding multi-loci associations among Single Nucleotide Polymorphisms (SNPs) and a disease. In this paper it is investigated whether Self Organizing Maps (SOMs) can generate clusters associated with a disease based on the genetic patterns of subjects. A batch categorical SOM that can handle missing data was used on Genome Wide Association (GWA)...
Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation,...
Early detection of onset and outbreak of infectious diseases has paramount importance in containing such diseases before they turn into epidemics. The incredible growth in popularity and spatial resolution of coverage have made micro-blogging sites like Twitter a promising source of information for assessing the evolution of intensity of such diseases within a locality. However, identifying tweets...
This paper discusses a correspondence between the core ideas of rough sets and medical differential diagnosis. Classically, a disease is defined as a set of symptoms, each of which gives the degree of confidence and coverage for the diagnosis. Diagnostic procedure mainly consists of the following three procedures: First, focusing mechanism (characterization) selects the candidates of differential...
Allergic rhinitis is a prevalent disease throughout the world. Electrodermal screening devices (EDSD) are devices that can measure the electrical properties of acupuncture points. This paper performs a series of experiments based on machine learning algorithms to study the feasibility of utilizing EDSD to diagnose allergic rhinitis. The experimental result shows that, to assess the presence of allergic...
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