Sima Shahsavari - Department of Computer Engineering Faculty of Engineering, Alzahra University Tehran, Iran
MohammadReza Keyvanpour - Department of Computer Engineering Faculty of Engineering, Alzahra University Tehran, Iran
Seyed Vahab Shojaedini - Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran, Iran

Recently thermography Imaging has been introduced as an effective tool for early detection of breast cancer. A vital step of this process is classifying captured images into healthy and sick categories which its main challenge is the lack of visual difference between these two types of images in many samples. Although deep learning methods may lead to better results than their classical alternatives in classifying thermography images but unfortunately two factors may hamper their effectiveness including the gradient vanishing as well as high dependence of the performance due to the volume of training and test data. In this article, the combination of Residual and Transfer Learning schemes is utilized in order to improve the performance of deep learning schemes against two above problems. The obtained results from applying the proposed scheme on real thermography images show that it may improve the detection parameters at least ۱۳.۵, ۷.۷ and ۱۸.۷ percent superiority against the best non-transfer-learning results in terms of accuracy, sensitivity and specificity respectively.

breast cancer, thermography, transfer learning, deep learning