Subclass based parallel learning neural network for classification of masses in mammograms

Abstract

Computer aided detection assists radiologists by providing second opinion in the mammography detection, and reduce misdiagnosis. An expert system with novel subclass based learning multiple neural network classifier (SBLMNN) has been proposed to solve the mammogram mass classification problem. This work explores the significance of the modular learning in artificial neural networks, inspired from the visual cortex basis of human learning. It is a two stage learning process. In stage I, the proposed architecture processes parallel on the radiological characteristics of mass like shape, margin and texture features in separate modules similar to the visual cortex to identify the subclasses. The intermediate outputs of the independent modules are processed to classify the mass into benign or malignant in stage II. Modularization and deep learning considered in the proposed method improves the performance of the classifier and speed of learning. For the experimental analysis, images are obtained from the mammogram image analysis society. The experiments were implemented in MATLAB. For benign and malignant classification, the shows that SBLMNN accuracy is 92%, which is higher than monolithic MLP neural network architecture.