Enhancing breast cancer diagnostics: Shape-aware angular feature learning for precision in breast cancer classification.
Authors
Affiliations (3)
Affiliations (3)
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia. Electronic address: [email protected].
- Department of Communication Design, National Institute of Design, Andhra Pradesh (NID-AP), Guntur 522510, India. Electronic address: [email protected].
- Department of Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur 495009, India. Electronic address: [email protected].
Abstract
Breast cancer is a life-threatening disease that is very common among women in the world. The early and correct diagnosis is necessary to enhance the rate of survival and treatment. The conventional techniques such as mammography, ultrasound and MRI usually fail to differentiate between the benign and malignancy tumors in the presence of limited resources, particularly in the resource-poor environments. This paper presents a new methodology of breast cancer classification, Shape-Aware Angular Feature Learning (SAAFL), that merges machine and deep learning methods. We propose a method of Speckle-Reducing Anisotropic Diffusion (SRAD) filters to improve the quality of ultrasound images by reducing the number of speckle noise and maintaining the edges of the tumors. Our segmentation approach is robust, which is RBBSAM-RSF, using which we detect tumors automatically and then process the data with Angular Feature (AF) analysis in order to determine the specific features of the lesions. The hierarchical classification system combines the AF extraction and classifiers like Support Vector Machines (SVM) and Backpropagation Artificial Neural Networks (BPANN) to enhance the accuracy of the diagnostic. On 1293 breast ultrasound (BUS) images, our model manages to attain 95.38 % accuracy, which is better than the traditional texture-based and morphological models. This reduces false positives and unnecessary biopsies, making it suitable for near-real-time deployment in low-resource clinical settings without specialized hardware. By selectively integrating deep learning-assisted segmentation with shape-aware angular feature analysis and supervised machine learning, SAAFL advances noninvasive and interpretable breast cancer diagnostics.