Benign and malignant mammographic image classification based on Convolutional Neural Networks

Computerized breast cancer diagnosis system has played an import role in early cancer diagnosis. For this purpose, we apply deep learning by using convolutional neural networks (CNN) to classify abnormalities, benign or malignant, in mammographic images based on the mini Mammographic Image Analysis Society (mini-MIAS) database. Accuracy, sensitivity, and specific values are observed to evaluate the performance of the CNN. To improve the performance, we utilize image-preprocessing methods containing cropping, global contrast normalization, augmentation, local histogram equalization, and balancing preprocessing. We built four CNN models to study the impact of depth and hidden layer structure on model performance. The CNN-4d model performs best among four proposed CNN models consisting of four convolution layers with a dropout of 0.7. The CNN-4d model achieved a balance of high sensitivity (90.63%) and high specificity (87.67%), and an accuracy of 89.05%. The result of this study indicates that CNNs have promising potential in the field of intelligent medical image diagnosis.