Intelligent Diagnosis of Breast Cancer with Thermograms using Convolutional Neural Networks

Abstract

Breast cancer is a serious public health issue among women all over the world. The main methods of breast cancer diagnosis include ultrasound, mammography and Magnetic Resonance Imaging (MRI). However, the existing methods of diagnosis are not appropriate for regular mass screening in short intervals. On the other hand, there is one non-invasive and low-cost method for mass and regular screening which is the so-called thermography. Recent studies show rapid quality improvement of thermal cameras as well as distinct development of machine learning techniques that can be combined together to enhance the technology of breast cancer detection. Machine learning technologies can potentially be used to support the interpretation of thermal images and help physicians to automatically determine the locations and sizes of tumors, blood perfusion, and other patient-specific properties of breast tissues. In this study, we aim to develop CNN techniques for intelligent precision breast tumor diagnosis. The main innovation of our work is the use of breast thermograms from a multicenter database without preprocessing for binary classification. The results presented in this paper highlight the usefulness and efficiency of deep learning for standardized analysis of thermograms. It is found that the model developed can have an accuracy of 80.77%, sensitivity of 44.44 % and the specificity of 100%.