Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks

Abstract

Early diagnosis of breast cancer is the most reliable and practical approach to mitigate cancer. Computer-aided detection or computer-aided diagnosis is one of the software technologies designed to assist doctors in detecting or diagnosing cancer and to reduce mortality using medical image analysis. Recently, Convolution Neural Networks became very popular in medical image analysis helping to process vast amount of data to detect and classify cancer in a fast and efficient manner. In this paper, we implemented deep neural networks ResNet18, InceptionV3 and ShuffleNet for binary classification of breast cancer in histopathological images. We have used networks pre-trained by the transfer learning on the ImageNet database and with fine-tuned output layers trained on histopathological images from the public dataset BreakHis. The highest average accuracy achieved for binary classification of benign or malignant cases was 98.73% for ResNet18, followed by 97.65% for ShuffleNet and 97.44% for Inception-V3Net.