Deep learning model for fully automated breast cancer detection system from thermograms

Esraa A. Mohamed, Essam A. Rashed, Tarek Gaber, Omar Karam, Robertas Damaševičius
2022 PLoS ONE  
Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the
more » ... t of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.
doi:10.1371/journal.pone.0262349 pmid:35030211 pmcid:PMC8759675 fatcat:3im3likw2vdgpjlm252hf5gil4