Breast Thermograms Analysis using Deep Neural Network

Abstract

Breast cancer is one of the leading causes of mortality among women globally. Early identification of this kind of cancer is important for a successful treatment outcome. A variety of screening techniques can be used to identify breast cancer. Thermography is possible for early diagnosis that uses thermal cameras with great resolution and sensitivity. The goal is to develop a system that automatically captures and classifies thermographic imaging of the breast as normal or abnormal. In this method,the detection and classification of breast cancer from thermography images usingadeeplearning-based convolution neural network using the VGG-19 algorithm. Breast cancer detection is performed using CNN with the help of Google collaboratory (Google Colab). Finally, as the result of experimental studies, the major focus is on the performance accuracy of the train and test dataset, and the graph is plotted between the training and validation accuracy.The VGG-19 network achieved the highesttestperformanceof 99.80% in breast cancer detection and to classify cancer using the DMR Mastology Research dataset.