Smart Diagnosis: AI and ML Powered Breast Cancer Classification

Background Breast cancer remains one of the leading causes of cancer-related morbidity and mortality among women worldwide. Early and accessible diagnostic tools are essential, particularly in low-resource settings where expert interpretation of ultrasound imaging may be limited. This study presents an AI- and machine learning–assisted web-based platform designed to support preliminary breast cancer lesion classification. Methods The study aimed to design and implement a Flask-based web application that accepts breast ultrasound images and patient metadata for pre-classification into benign, malignant, or normal categories. Images were preprocessed using standardized resizing and normalization, while probabilistic classification was simulated using a NumPy-based framework. Interactive visualizations, confidence scoring, and PDF report generation were incorporated to enhance clinical usability. Results The platform demonstrated rapid and consistent performance, with image processing and prediction latency ranging from 0.018 to 0.06 seconds. Simulated batch analysis showed clear separation between cancer-positive and healthy cases, with higher median confidence scores for malignant samples. The system successfully generated intuitive dashboards, region-of-interest overlays, and downloadable diagnostic reports. Conclusions This prototype highlights the feasibility of integrating AI-assisted image analysis with user-friendly web interfaces for early breast cancer screening. Although current predictions are simulated, the modular architecture allows seamless integration of real deep learning models in the future. The platform has potential implications for improving early detection, patient awareness, and equitable access to diagnostic support.