Breast cancer diagnosis from thermal images

Breast cancer is one of the prevalent types of cancer. Early diagnosis and treatment of breast cancer have vital importance for patients. Various imaging techniques are used in the detection of cancer. Thermal images are obtained by using the temperature difference of regions without giving radiation by the thermal camera. In this study, we present methods for computer aided diagnosis of breast cancer using thermal images. To this end, various Convolutional Neural Network (CNN) models have been designed by using transfer learning methodology. The performance of the designed nets was evaluated on a benchmarking dataset considering accuracy, precision, recall, F1 measure, and Matthews Correlation coefficient. The results show that holding pre-trained convolutional layers and training newly added fully connected layers gives the best scores. We have obtained an accuracy of 94.3%, a precision of 94.7% and a recall of 93.3% using transfer learning methodology with CNN.