Real-time thermography for breast cancer detection with deep learning

Abstract

In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.