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Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
Cytogenetics plays a central role in the detection of chromosomal abnormalities and in the diagnosis of genetic diseases. A karyogram is an image representation of human chromosomes arranged in order of decreasing size and paired in 23 classes. In this paper we propose an approach to automatically pair the chromosomes into a karyogram, using the information obtained in a rough SVM-based classification...
This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the calculation of the skewness to each m-by-m sliding block of the transaxial slices of the SPECT brain images. We replace the center pixel in the m-by-m block by the skewness value and build a new 3-D brain image which will...
Magnetic resonance image (MRI) has been widely used for clinical applications in recent years. With the ability of scanning the same section by multiple frequencies, MRI makes it possible to generate several images on the same section. Despite of accessible abundant information, MRI also makes it more difficult to judge the location of every tissue. MRI will complicate the judgment due to strong noise...
Breast cancer is the most common cancer among women. To assist the ultrasound (US) diagnosis of solid breast tumors, the lobulated contour feature quantified by boundary-based corner counts is studied to classify breast tumors as malignant or benign. The corner points in this research was detected based on wavelet transform (WT), and the classification selected through comparison is support vector...
Colonic polyps appear as elliptical protrusions on the inner wall of the colon. Previous algorithms assumed the shape of a polyp to be a spherical cap, so these algorithms are not flexible when the polyps are various cap shapes. This paper proposes an explicit parametric model for the polyps. The model captures the overall shape of the polyp and is used to derive the probability distribution of features...
In this paper we present a method for developing a fully automated computer aided diagnosis (CAD) system to help radiologist in detecting and diagnosing micro-calcifications (MCCs) in digital format mammograms. One aim of the CAD system is to increase the effectiveness and efficiency of screening procedures by using computer. Another aim of the CAD is to extract and analyze the characteristics of...
Diabetic retinopathy is a leading cause of blindness in developed countries. Diabetic patients can prevent severe visual loss by attending regular eye examinations and receiving timely treatments. In the United States, standard protocols have been developed and refined for years to provide better screening and evaluation procedures of the fundus images. Due to the emerging number of diabetic retinopathy...
Various techniques have been developed for texture classification which can be used for an automated classification of endoscopic images. A certain subset of these techniques is applied to duodenal imagery for diagnosis of celiac disease. Spatial domain (histogram) and transform domain (wavelet) features are extracted from the images for subsequent classification with various algorithms (KNN, SVM,...
Ultrasound imaging has found its own place in medical applications as an effective diagnostic tool. Ultrasonic diagnostics has made possible the detection of cysts, tumors or cancers in abdominal organs. In this paper, the possibilities of an automatic classification of ultrasonic liver images by optimal selection of texture features are explored. These features are used to classify these images into...
The ability to quantify structural attributes using cellular neural networks (CNN) has been shown for a wide range of objects. We here introduce an application that allows the detection of structural alterations in the human brain. Using a CNN-based classification approach we show that a defined class of abnormalities - the so called hippocampal sclerosis - can be detected in T1-weighted magnetic...
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