Jafari, Fatemeh, Ghafari, Hamidreza, Farsi, Hassan. (1404). Detection of Breast Cancer Masses in Mammography Images using a Hybrid Faster R-CNN and Fuzzy Logic Framework. فناوری آموزش, 14(2), 507-518. doi: 10.22061/jecei.2026.12577.892
Fatemeh Jafari; Hamidreza Ghafari; Hassan Farsi. "Detection of Breast Cancer Masses in Mammography Images using a Hybrid Faster R-CNN and Fuzzy Logic Framework". فناوری آموزش, 14, 2, 1404, 507-518. doi: 10.22061/jecei.2026.12577.892
Jafari, Fatemeh, Ghafari, Hamidreza, Farsi, Hassan. (1404). 'Detection of Breast Cancer Masses in Mammography Images using a Hybrid Faster R-CNN and Fuzzy Logic Framework', فناوری آموزش, 14(2), pp. 507-518. doi: 10.22061/jecei.2026.12577.892
Jafari, Fatemeh, Ghafari, Hamidreza, Farsi, Hassan. Detection of Breast Cancer Masses in Mammography Images using a Hybrid Faster R-CNN and Fuzzy Logic Framework. فناوری آموزش, 1404; 14(2): 507-518. doi: 10.22061/jecei.2026.12577.892
1Department of Computer Engineering, Ferdows Branch, Islamic Azad University, Ferdows, Iran.
2Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 12 آبان 1404،
تاریخ بازنگری: 10 بهمن 1404،
تاریخ پذیرش: 28 بهمن 1404
چکیده
Background and Objectives: Breast cancer is a leading cause of mortality among women worldwide. Early detection plays a pivotal role in reducing mortality rates and improving patient outcomes by identifying risk factors, enhancing screening methods, and enabling timely treatment. Recent advances in artificial intelligence (AI) and deep learning have facilitated accurate and efficient analysis of medical images, supporting rapid and precise breast cancer detection. This study aims to develop a fast and reliable approach for detecting breast cancer masses in mammography images using a deep learning framework. Methods: The proposed approach employs a Faster R-CNN architecture with a ResNet backbone for robust feature extraction. Fuzzy logic is integrated to adaptively adjust the learning rate, improving training stability. Transfer learning and data augmentation techniques are applied to enhance model generalization and reduce overfitting. The method labels affected regions in mammography images, enabling accurate localization of cancerous areas. Results: Experiments were carried out using the CBIS-DDSM dataset. The proposed model demonstrated a cancer detection accuracy of 97.84%, an Intersection over Union (IoU) of 98.12%, and a mAP50 of 0.83, highlighting its exceptional performance in accurately localizing breast cancer masses. Conclusion: The integration of Faster R-CNN with ResNet, fuzzy logic-based learning rate adaptation, transfer learning, and data augmentation yields a highly effective solution for automated breast cancer detection. The results highlight the potential of this method to improve early diagnosis and support clinical decision-making in breast cancer screening.
