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Convolutional Neural Network Design for Breast Cancer Medical Image Classification

For computer-aided diagnosis of medical issues, image classification plays an important role to ensure high prediction accuracy. Convolutional neural network (CNN) is an important aspect of deep learning because it returns a higher performance rate than other traditional machine learning methods and solves end-to-end classification problems Recently, machine learning and artificial intelligence have rapidly advanced in different fields, especially in the health industry. One of the main medical problems in the world today arises from diseases such as brain tumors, breast cancer, and lung cancer. Therefore, this paper aims at employing image classification and medical image segmentation based on deep learning to detect such medical issues on time. In this paper, a 15-layer CNN model is designed to classify and recognize medical images; especially, breast cancer cells, by using relevant CNN classification techniques. The experimental results and supporting data show that the proposed model achieves better performance in terms of higher validation accuracy and training accuracy, as well as lower losses in training and testing, in comparison with other traditional CNN models including AlexNet and VGGNet.