Deep Learning-Enhanced Ultrasound Analysis: Classifying Breast Tumors using Segmentation and Feature Extraction

Abstract Background Breast cancer remains a significant global health challenge, demanding accurate and effective diagnostic methods for timely treatment. Ultrasound imaging stands out as a valuable diagnostic tool for breast cancer due to its affordability, accessibility, and non-ionizing radiation properties. Methods We evaluate the proposed method using a publicly available breast ultrasound images. This paper introduces a novel approach to classifying breast ultrasound images based on segmentation and feature extraction algorithm. The proposed methodology involves several key steps. Firstly, breast ultrasound images undergo preprocessing to enhance image quality and eliminate potential noise. Subsequently, a U-Net + + is applied for the segmentation. A classification model is then trained and validated after extracting features by using Mobilenetv2 and Inceptionv3 of segmented images. This model utilizes modern machine learning and deep learning techniques to distinguish between malignant and benign breast masses. Classification performance is assessed using quantitative metrics, including recall, precision and accuracy. Our results demonstrate improved precision and consistency compared to classification approaches that do not incorporate segmentation and feature extraction. Feature extraction using InceptionV3 and MobileNetV2 showed high accuracy, with MobileNetV2 outperforming InceptionV3 across various classifiers. Results The ANN classifier, when used with MobileNetV2, demonstrated a significant increase in test accuracy (0.9658) compared to InceptionV3 (0.7280). In summary, our findings suggest that the integration of segmentation techniques and feature extraction has the potential to enhance classification algorithms for breast cancer ultrasound images. Conclusion This approach holds promise for supporting radiologists, enhancing diagnostic accuracy, and ultimately improving outcomes for breast cancer patients. In future our focus will be to use comprehensive datasets to validate our methodology.