Radio thermal Dual-Imaging Fusion Network for Deep Feature-Driven Breast Cancer Detection - K., Suriya; R., Praveen Kumar; V., Nithyashree; S., Dhanusri; K., Abinaya; A., Ranjana (Journal of Innovative Image Processing, 2025)
Breast cancer is one of the major problems affecting the breast that is very commonly detected in females, and it requires efficient and precise diagnosis for enhanced stages of survival. To achieve efficient, precise, and contactless diagnostic processes, it is proposed that the Radio Thermal Dual-Imaging Fusion Framework be utilized in combination with the structural information obtained from mammogram images, along with thermal information obtained from infrared images of thermograms of the breasts. This paper proposes a conceptual design that makes use of an available set of mammogram images taken from digital image databases, such as The Mammographic Image Analysis Society Database (MIAS), standard sets of samples from the curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), as well as samples from anonymized patients in recent routine clinical screenings, similar to recent diagnostic studies that utilized mammography. The samples for thermal images were obtained from infrared images of thermograms of breasts, which are still very much in use in today’s breast cancer diagnostic research. The proposed conceptual design for diagnosis will employ the Super-Fast Recurrent Convolutional Neural Network (SFRCNN) architecture, which will be a ResNet-50 based architecture for the extraction of thermal images and standard images, as mentioned above. The preprocessing for sampling the modalities will be done using grayscale normalization of the standard mammogram images as well as standard thermal mapping for the infrared images. From the results of the proposed conceptual design, it has been identified that the proposed dual-imaging modality accuracy of 84.75% will be obtained by using the standard mammogram images and the standard infrared images, representing a significant improvement over standard image processing, as an improvement in accuracy of 3% is expected from the proposed standard approaches within the benchmark design for the Convolutional Neural Network model due to the proposed dual-imaging modality technique, along with a proposed diagnosis accuracy for assessing a sensitivity of 98.04% by the dual-imaging modalities of the standard infrared images.