Document Type : Original Article

Author

• ali ghanbari sorkhi

Department of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Mazandaran, Iran

Abstract

Given the importance of breast cancer detection and the increasing prevalence of this disease, along with its high annual mortality rate, extensive research has been conducted in recent years on medical image analysis for this purpose. In this paper, a multi-stage method is presented based on image segmentation of healthy and unhealthy (cancerous) tissues and a hybrid classification approach for determining the type of cancer (benign or malignant). In the proposed method, after noise reduction, an improved U-Net model is employed for image segmentation and detection of tumor candidate regions. For images identified as unhealthy, contour-based feature extraction is applied, followed by a hybrid ensemble classification method using majority voting among base classifiers to determine the cancer type.

The proposed approach has been evaluated on a standard ultrasound image dataset consisting of healthy, benign, and malignant samples. The proposed method achieved a segmentation accuracy of 97.43% using the enhanced U-Net and an overall system accuracy of 94% for breast cancer diagnosis, outperforming other recent state-of-the-art techniques on the same dataset.

Keywords

• Breast Cancer
• Improved U-Net
• Multi-Stage Classification
• Hybrid Classification
• Ultrasound Images