Precision healthcare in action: A robust MRI framework for breast cancer diagnosis with enhanced feature analysis and boundary refinement
Akbar A. Han S. Ybytayeva G. Ahmed K. Rehman N.U. Alzahrani K.J. Algarni A. Mostafa H.A.
1 July 2026Elsevier Ltd
Biomedical Signal Processing and Control
2026#120
Several advanced deep-learning techniques have been developed for breast cancer classification and segmentation. However, challenges such as tumor size variations, class imbalance, and boundary detection often limit their performance. A key limitation lies in the inability to retain location information during feature extraction, coupled with increased model complexity, which escalates computational demands and reduces batch size, further impacting performance. To address these issues, our proposed framework introduces advanced MRI pre-processing and an optimized encoder–decoder architecture. The encoder extracts features, while the decoder reconstructs the image. Efficient feature extraction is achieved through a Semantic Segmentation Module (SSM) with separable Conv2D, reducing computational load. The Essential Feature Attention Module (EFAM) and Deep Residual Module (DRM) further reduce parameters and enhance restoration accuracy by addressing the limitations of traditional U-Net models. The framework employs EFAM in place of concatenation for improved restoration and iterative correction via DRM to prevent input vanishing. Upsampling restores images to their original size, producing a three-channel output for normal, benign, or malignant classes using a sigmoid function. A tailored objective function, combining Dice Score and Weighted Cross Entropy (WCE) which mitigates class imbalance and enhances boundary detection. Evaluations on benchmark datasets demonstrated superior Dice scores: 93.164%, 94.175%, and 89.772% for benign classes and 95.086%, 97.902%, and 93.078% for malignant classes on BreastDM, Duke, and RIDER datasets, respectively. The proposed framework ensures accurate segmentation and classification across varying tumor sizes.
Boundary detection accuracy , Breast cancer , Computer-aided diagnosis , Deep learning , Feature extraction optimization , Medical image processing
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School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, Henan, 450001, China
Department of Technical and Natural Sciences, International Educational Corporation, Almaty, 050043, Kazakhstan
Kazakh Leading Academy of Architecture and Civil Engineering, Almaty, Kazakhstan
School of Software, Henan University, Kaifeng, 475001, China
School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, 450001, China
Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif, 21944, Saudi Arabia
Department of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia
Faculty of Computer and Artificial Intelligence, Fayoum University, Fayoum, 63514, Egypt
Khaybar Applied College, Taibah University, Medina, 42353, Saudi Arabia
School of Electrical and Information Engineering
Department of Technical and Natural Sciences
Kazakh Leading Academy of Architecture and Civil Engineering
School of Software
School of Computer Science and Artificial Intelligence
Department of Clinical Laboratories Sciences
Department of Computer Science
Faculty of Computer and Artificial Intelligence
Khaybar Applied College
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