Self-Supervised Bi-Pipeline Learning Approach for High Interpretation of Breast Thermal Images

The image quality supports a high accuracy rate of medical image diagnosis using computer vision. Digital thermal images resulting from the thermal device usually suffer from many watermarks that may lower the neural network learning performance. Thus, providing only the region of interest (RoI) of the breast area from the breast thermal images for early breast cancer detection is an important task. The goal of our work are to develop a deep learning (DL) model for taking the RoI of the breast thermal images, built a self-supervised DL model to classify the breast thermal images into healthy and cancer categories, and integrated these two models as end-to-end bi-pipeline model for breast thermal image recognition. The segmentation model was built using attention U-Net with residual recurrent network called R2AU-Net, and the classification model was built using self-supervised learning consisting of the Simple Framework for Contrastive Learning of Visual Representations (SimCLR) and ResNet50. These networks were trained using unlabelled limited breast thermal datasets to allow more comprehensive learning. The result shows that proposed self-supervised bi-pipeline model can take the RoI with an accuracy rate of 98.63% and classify the breast thermal images with a top-1 accuracy rate of 84.37% and top-5 accuracy rate of 96.87%. In addition, the bi-pipeline model implementation using a central processing unit shows that the model required only about 4 seconds for segmentation and classification tasks. These findings indicate that the bi-pipeline model can effectively aid the interpretation of unlabeled breast thermal images.