Enhancing Breast Cancer Diagnosis: A CNN-Based Approach for Medical Image Segmentation and Classification

This study introduces a novel Convolutional Neural Network (CNN) approach for breast cancer diagnosis, which seamlessly integrates segmentation and classification. The segmentation process achieves high precision, with Jaccard Index (JI) values of 0.89, 0.92, and 0.87 for Normal, Benign, and Malignant regions, respectively, resulting in an overall JI of 0.896. Similarly, the Dice Similarity Coefficient (DSC) values are notably high, with 0.94, 0.96, and 0.92 for the corresponding regions, yielding an overall DSC of 0.943. The CNN model exhibits high accuracy, specificity, precision, recall, and F1 score across all classes, establishing its reliability for clinical applications. This research comprehensively evaluates the model’s performance metrics, addressing challenges in breast cancer diagnostics and proposing an innovative CNN-based solution. Beyond immediate applications, it lays a robust foundation for future medical imaging advancements, enhancing diagnostic accuracy and patient outcomes.