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Enhancing breast cancer diagnostics: Shape-aware angular feature learning for precision in breast cancer
Abdul Khader Jilani Saudagar1, Abhishek Kumar2, Ankit Kumar3
1Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.
Computational Biology and Chemistry
|February 17, 2026
Summary
A new Shape-Aware Angular Feature Learning (SAAFL) method improves breast cancer classification using machine and deep learning. This technique enhances diagnostic accuracy in ultrasound images, reducing unnecessary biopsies.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Breast cancer diagnosis is critical for survival, but conventional methods struggle in resource-limited settings.
- Differentiating benign from malignant tumors remains a challenge, leading to potential misdiagnosis and unnecessary procedures.
Purpose of the Study:
- To introduce a novel methodology, Shape-Aware Angular Feature Learning (SAAFL), for accurate breast cancer classification.
- To enhance the performance of breast ultrasound (BUS) image analysis, particularly in resource-poor environments.
Main Methods:
- Speckle-Reducing Anisotropic Diffusion (SRAD) filters were used to denoise ultrasound images.
- A robust segmentation approach (RBBSAM-RSF) enabled automatic tumor detection.
- Angular Feature (AF) analysis and a hierarchical classification system combining Support Vector Machines (SVM) and Backpropagation Artificial Neural Networks (BPANN) were employed.
Main Results:
- The SAAFL model achieved 95.38% accuracy on 1293 breast ultrasound images.
- The proposed method outperformed traditional texture-based and morphological models.
- The approach demonstrated reduced false positives and the potential for fewer unnecessary biopsies.
Conclusions:
- SAAFL offers an accurate, noninvasive, and interpretable method for breast cancer diagnostics.
- The model is suitable for near-real-time deployment in low-resource clinical settings.
- Integration of deep learning segmentation with shape-aware feature analysis advances breast cancer detection.
