Classification of static infrared images using pre-trained CNN for breast cancer detection
Caroline Gonçalves, Jefferson R. Souza, Henrique Fernandes
Abstract
Breast cancer is a disease that affects many women throughout the world. It is the second most common type of cancer. The early diagnosis of the disease is relevant for increasing the chances of the patient recovering. Thermography is a promising technique that might be used to help the early diagnosis of breast cancer. In this work, we use three state of the art CNNs (VGG-16, Densenet201, and Resnet50) combined with transfer learning to classify static thermography images (sick and healthy). In our experiments, the best results have an F1-score of 0.92, 91.67% for accuracy, 100% for sensitivity, and 83.3% for specificity obtained with the Densenet using 38 static images for each class.