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.

中文翻译：

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基于子类的并行学习神经网络，用于乳房X线照片质量分类

计算机辅助检测可在X线摄影检测中提供第二意见，从而帮助放射科医生，并减少误诊。提出了一种基于新颖子类的学习多神经网络分类器（SBLMNN）的专家系统，以解决乳房X线照片的质量分类问题。这项工作探索了基于人工学习的视觉皮层基础的人工神经网络中模块化学习的重要性。这是一个分为两个阶段的学习过程。在第一阶段，所提出的体系结构在类似于视觉皮层的单独模块中并行处理质量的放射学特征（如形状，边界和纹理特征）以识别子类。在阶段II中，对独立模块的中间输出进行处理以将质量分为良性或恶性。提出的方法中考虑的模块化和深度学习提高了分类器的性能和学习速度。为了进行实验分析，从乳房X射线照片图像分析协会获得图像。实验是在MATLAB中实现的。对于良性和恶性分类，表明SBLMNN的准确性为92％，高于整体式MLP神经网络体系结构。