Automated Diagnosis system for detection of the pathological brain using Fast version of Simplified Pulse-Coupled Neural Network and Twin Support Vector Machine

Abstract

Brain abnormalities are neurological disorders of the human nervous system that contain biochemical, electrical, and structural changes in the brain and spinal cord. However, such changes produce diverse symptoms like paralysis, amnesia, and muscle weakness. The diagnosis of these abnormalities is crucial for treatment planning in the early stage to limit the progression of diseases. The brain Magnetic Resonance (MR) images are extensively used for treatment planning, but manually diagnosis of MR images is a time-consuming, expensive, and cumbersome task. Hence, in this paper, we have proposed the automated Computer-Aided Diagnosis (CAD) system for classification of brain MR images. These images are skill-stripped for removing the irrelevant tissues that improve the quality of images. We have developed the Fast version of Simplified Pulse-Coupled Neural Network (F-SPCNN) to segment the region of interest. Further, the features are extracted from the segmented images by using the Ripplet Transform (RT). Subsequently, Probabilistic Principal Component Analysis (PPCA) is employed for reducing the dimensionality of features. Finally, Twin Support Vector Machine (TWSVM) is applied for classification of brain MR images. The extensive simulation results on three standard datasets, e.g., DS-66, DS-160, and DS-255, demonstrate that the proposed method achieves better performance than the state-of-the-art methods.