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In this paper, Metaheuristic Algorithms such as Genetic Algorithm (GA) and Parallel Ant Colony Optimization (ACO) are implemented to extract the suspicious region based on the asymmetry approach. In bilateral subtraction, the asymmetries between corresponding left and right breast images are considered for extracting the suspicious region from the background tissue. The breast border and the nipple...
In this paper we propose a new approach for automated diagnosis and classification of Magnetic Resonance (MR) human brain images, using Wavelets Transform (WT) as input to Genetic Algorithm (GA) and Support Vector Machine (SVM). The proposed method segregates MR brain images into normal and abnormal. Our contribution employs genetic algorithm for feature selection witch requires much lighter computational...
Osteoporosis is considered as a major public health threat. It is characterized by a decrease in the density of bone, decreasing its strength and leading to an increased risk of fracture. In this work, the morphological, topological and mechanical characteristics of 2 populations of arthritic and osteoporotic trabecular bone samples are evaluated using artificial intelligence and recently developed...
This work describes the feasibility of multiresolution feature selection and its application to classify ultrasonic liver images. The proposed approach uses genetic algorithm and defines a novel fitness function for medical applications since the diagnosis correctness is the most important consideration. Via the measurement of class seperability, we can uniquely select the feature vector. The effectiveness...
Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier,...
Feature extraction is an important issue for analysis of gene expression microarray data, of which principle component analysis (PCA) is one of the frequently used methods, and in the previous works, the top several principle components are selected for modeling according to the descending order of eigenvalues. In this paper, we argue that not all the first features are useful, but features should...
We present a computational framework to identify prostate specific cancer biomarkers using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue imaging data collected at the Eastern Virginia Medical School (EVMS). Protein profiles of a tumor and its surrounding area from one prostate tissue sample were analyzed. The data contain 974 spectra (27 cancer, 947 normal). We proposed...
Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following...
This paper proposes a computer aided decision support system for an automated diagnosis and classification of breast tumor using mammogram. The proposed method differentiates two breast diseases namely benign masses and malignant tumors. From the preprocessed mammogram image, texture and shape features are extracted. The optimal features can be extracted by using a feature selection scheme based on...
An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses/lesions appearing in breast in different groups of benignancy/malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm-based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing...
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