A deep learning approach for automatic detection, segmentation and classification of breast lesions from thermal images

Abstract

PurposeThis study investigates implementation of deep learning (DL) approaches to breast tumor recognition based on thermal images. We propose to utilize Mask R-CNN technique on images by first assigning bounding boxes and then creating a border for each tumor volume to differentiate it from adjacent tissues and structures. In this manner, thermal images can be handled by a single DL model to successfully perform detection, classification, and segmentation of normal and abnormal breast tissues. MethodsThis study employs Mask R-CNN technique along with transfer learning models to accurately delineate breast volumes from adjacent tissues and structures. It is a novel study that uses a single DL model to carry out three steps of breast tumor diagnosis, namely detection, segmentation and classification of normal and abnormal tissues based on thermal images. ResultsTwo network architectures (ResNet-50 and ResNet-101) were trained for 60 epochs and were then evaluated based on their classification and segmentation performances. The testing process resulted in higher classification success for ResNet-50 backbone pretrained on COCO images (%97.1 accuracy). Detection and segmentation performances of this model were also higher with mAP of 0.921 and overlap score of 0.868. ConclusionsOur results indicate that the classification and segmentation performances of Mask R-CNN method on ResNet-50 architecture are better than the data reported in the literature for thermal breast image studies. It should be emphasized that our approach employs a single DL model that successfully performs detection, classification, and segmentation procedures for diagnosing normal and abnormal breast tissues.