Deep Convolutional Neural Network for Computer-Aided Detection of Breast Cancer Using Histopathology Images
R. Karthiga, K Narashimhan
Abstract
Abstract The innovation in medical imaging technologies leads to a frenetic pace of change in health care. In recent years various deep learning algorithms play a significant role in medical image classification and diagnosis. The deep convolutional neural network (DCNN) has obtained impressive results in many health-related applications. The fine-tuning parameters and weight initialization is the major task to adapt pre-trained convolution models. We explored transfer learning approaches using Alexnet, and VGG-16 analyzed with their behavior. Also, the DCNN framework had developed and compared with Alex net and VGG-16 transfer learning models. The DCNN attained more significant results compare to transfer learning models. The DCNN procures outstanding performance for binary (93.38%) and multi-class (average 89.29%), which exceeds the previous state of the art techniques in the literature.