PDFBreast 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.
@article{k.2025,
author = {Suriya K. and Praveen Kumar R. and Nithyashree V. and Dhanusri S. and Abinaya K. and Ranjana A.},
title = {{Radio thermal Dual-Imaging Fusion Network for Deep Feature-Driven Breast Cancer Detection}},
journal = {Journal of Innovative Image Processing},
volume = {7},
number = {4},
pages = {1460-1481},
year = {2025},
publisher = {Inventive Research Organization},
doi = {10.36548/jiip.2025.4.020},
url = {https://doi.org/10.36548/jiip.2025.4.020}
}
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- Nicolis, Orietta, Denisse De Los Angeles, and Carla Taramasco. "A contemporary Review of Breast Cancer Risk Factors and the Role of Artificial Intelligence." Frontiers in Oncology 14 (2024): 1356014.
- R. Sh a, Y. Zhang, L. Wang, X. Li, and Z. Liu, “Global Burden of Breast Cancer and Attributable Risk Factors in Women: New Estimates for 2021,” Biomarker Research, vol. 12, no. 1, 2024, 1–15.
- Gilbert, Fiona J., and Katja Pinker-Domenig. "Diagnosis and Staging of Breast Cancer: When and How to Use Mammography, Tomosynthesis, Ultrasound, Contrast-Enhanced Mammography, and Magnetic Resonance Imaging." Diseases of the Chest, Breast, Heart and Vessels 2019-2022: Diagnostic and Interventional Imaging (2019): 155-166.
- Abeelh, Enas Abu, and Zain AbuAbeileh. "Comparative Effectiveness of Mammography, Ultrasound, and MRI in the Detection of Breast Carcinoma in Dense Breast Tissue: A Systematic Review." Cureus 16, no. 4 (2024).
- Yarabarla, Mamatha Sai, Lakshmi Kavya Ravi, and A. Sivasangari. "Breast Cancer Prediction Via Machine Learning." In 2019 3rd international conference on trends in electronics and informatics (ICOEI), IEEE, 2019, 121-124.
- Dabass, Jyoti, Shaveta Arora, Rekha Vig, and Madasu Hanmandlu. "Segmentation Techniques for Breast Cancer Imaging Modalities-A Review." In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, 2019, 658-663.
- Ibrahim, Abdelhameed, Shaimaa Mohammed, Hesham Arafat Ali, and Sherif E. Hussein. "Breast Cancer Segmentation from Thermal Images Based on Chaotic Salp Swarm Algorithm." IEEE Access 8 (2020): 122121-122134.
- Robin, Mirya, Jisha John, and Aswathy Ravikumar. "Breast Tumor Segmentation Using U-NET." In 2021 5th international conference on computing methodologies and communication (ICCMC), IEEE, 2021, 1164-1167.
- Khasana, Uswatun, Riyanto Sigit, and Heny Yuniarti. "Segmentation of Breast Using Ultrasound Image for Detection Breast Cancer." In 2020 International Electronics Symposium (IES), IEEE, 2020, 584-587.
- Nguyen, Thanh-Tam, Thanh-Hai Nguyen, Ba-Viet Ngo, and Duc-Dung Vo. "Breast Image Segmentation for Evaluation of Cancer Disease." In 2020 5th International Conference on Green Technology and Sustainable Development (GTSD), IEEE, 2020, 344-348.
- Chavez, Tanny, Nagma Vohra, Jingxian Wu, Magda El-Shenawee, and Keith Bailey. "Spatial Image Segmentation for Breast Cancer Detection in Terahertz Imaging." In 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, 2020, 1157-1158.
- Georgas, Konstantinos, Ioannis A. Vezakis, Ioannis Kakkos, Anastasia Natalia Douma, Evangelia Panourgias, Lia A. Moulopoulos, and George K. Matsopoulos. "Rad-EfficientNet: Improving Breast MRI Diagnosis Through Integration of Radiomics and Deep Learning." IEEE Journal of Biomedical and Health Informatics (2025).
- Al-Antari, Mugahed A., Mohammed A. Al-Masni, Mun-Taek Choi, Seung-Moo Han, and Tae-Seong Kim. "A Fully Integrated Computer-Aided Diagnosis System for Digital X-ray Mammograms Via Deep Learning Detection, Segmentation, And Classification." International journal of medical informatics 117 (2018): 44-54.
- Loza, Carlos A. "RobOMP: Robust Variants of Orthogonal Matching Pursuit for Sparse Representations." PeerJ Computer Science 5 (2019): e192.
- Khater, Tarek, Abir Hussain, Riyad Bendardaf, Iman M. Talaat, Hissam Tawfik, Sam Ansari, and Soliman Mahmoud. "An Explainable Artificial Intelligence Model for the Classification of Breast Cancer." IEEE Access (2023).
- Deng, Tianpeng, Chunwang Huang, Ming Cai, Yu Liu, Min Liu, Jiatai Lin, Zhenwei Shi et al. "FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis." IEEE Transactions on Medical Imaging (2025).
- Jiang, Jiale, Junchuan Peng, Chuting Hu, Wenjing Jian, Xianming Wang, and Weixiang Liu. "Breast Cancer Detection and Classification in Mammogram Using a Three-Stage Deep Learning Framework Based on PAA Algorithm." Artificial Intelligence in Medicine 134 (2022): 102419.
- Al-Masni, Mohammed A., Mugahed A. Al-Antari, Jeong-Min Park, Geon Gi, Tae-Yeon Kim, Patricio Rivera, Edwin Valarezo, Mun-Taek Choi, Seung-Moo Han, and Tae-Seog Kim. "Simultaneous Detection and Classification of Breast Masses in Digital Mammograms Via a Deep Learning YOLO-based CAD System." Computer methods and programs in biomedicine 157 (2018): 85-94.
- Alzahrani, Riyadh M., Mohamed Yacin Sikkandar, S. Sabarunisha Begum, Ahmed Farag Salem Babetat, Maryam Alhashim, Abdulrahman Alduraywish, N. B. Prakash, and Eddie YK Ng. "Early Breast Cancer Detection Via Infrared Thermography Using a CNN Enhanced with Particle Swarm Optimization." Scientific Reports 15, no. 1 (2025): 25290.
- Alshamrani, Khalaf, Hassan A. Alshamrani, Fawaz F. Alqahtani, and Bander S. Almutairi. "Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN." Sensors 23, no. 1 (2022): 235.
- Almalki, Yassir Edrees, Toufique Ahmed Soomro, Muhammad Irfan, Sharifa Khalid Alduraibi, and Ahmed Ali. "Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer." Sensors 22, no. 5 (2022): 1868.
- Abdelrahman, Leila, Manal Al Ghamdi, Fernando Collado-Mesa, and Mohamed Abdel-Mottaleb. "Convolutional Neural Networks for Breast Cancer Detection in Mammography: A Survey." Computers in biology and medicine 131 (2021): 104248.
- Sawyer-Lee, R., Gimenez, F., Hoogi, A., & Rubin, D. (2016). Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY.
- https://www.kaggle.com/datasets/kmader/mias-mammography
