AI-Driven Elastography Image Analysis for Accurate Breast Cancer Diagnosis
P. Parameswari · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: Breast cancer detection and classification are critical areas in medical imaging where precise diagnosis is essential for effective treatment and patient care. This study employs artificial intelligence (AI) techniques to tackle these challenges within elastography images. Combined with B-mode ultrasound, elastography provides valuable information on the stiffness and geometric characteristics of breast lesions, which aids in distinguishing between benign and malignant tumours. Our approach integrates AI algorithms, specifically supervised learning methods such as support vector machines (SVM), to develop a robust framework for automated breast lesion detection and classification. The process involves several key steps: extensive preprocessing of images, feature extraction, and dimensionality reduction using techniques like principal component analysis (PCA). To ensure the accuracy and reliability of the system, we subject it to rigorous validation through cross-validation methods. The results of our study indicate high accuracy rates, showcasing the potential of AI-driven solutions to improve breast cancer diagnosis and enhance patient outcomes