Multiscale Analysis Domain Interpretable Deep Neural Network for Detection of Breast Cancer Using Thermogram Images

Breast cancer is the most prevalent cancer among women, with a high mortality rate. The early detection of breast cancer using medical imaging techniques helps reduce the number of deaths caused by this disease. Thermogram imaging is safer and less expensive than mammography for diagnosing breast cancer. The automated analysis of thermogram images using artificial intelligence (AI) methods is an interesting approach to detect breast cancer. This article proposes a novel multiscale analysis domain interpretable deep learning (MSADIDL) approach for automatically detecting breast cancer using thermogram images. The 2D empirical wavelet transform (2DEWT) with fixed boundary points (FBPs) is employed for the multiscale analysis of thermogram images and evaluation of modes or subbands. All the modes of the thermogram images are used as the input to the MSADIDL model for the automated detection of breast cancer. The MSADIDL architecture comprises seven individual deep neural networks (DNNs) connected in parallel. The outputs of the individual DNNs are concatenated and then used as the input to the dense layers, after which the output layer evaluates the probability score for the automated categorization of normal versus cancerous classes. A publicly available thermogram imaging dataset is utilized to evaluate the performance of the proposed MSADIDL approach. The results show that the proposed MSADIDL approach has obtained an accuracy value of 99.54% for both fivefold cross-validation (CV) and hold-out validation cases using all seven modes of thermogram images. The MSADIDL model has achieved an accuracy higher than all of the transfer learning-based breast cancer detection techniques using thermogram images. The suggested MSADIDL model has shown higher accuracy when compared with different existing methods to detect breast cancer using thermogram images.