AI Enabled and Deep Learning-based Integrated Approach for Early Detection of Breast Cancer

The ongoing global health crisis of breast cancer needs prompt and precise diagnostic measures in order to improve outcomes. The routine practice of swiftly interpreting clinical images manually involves considerable time and variability in diagnostic accuracy owing to dissimilar observers. This work describes a fully integrated approach in which cancer detection and diagnostic reporting are automatically performed by AI systems from MRI video sequences. Our proposed system architecture includes client-side frame extraction, deep learning-based classification, distributed backend processing with parallel AI inference, and report generation in PDF format. The performance of our AI systems is clinically viable due to the achievement of clinically high-performance metrics (i.e., accuracy, sensitivity, and specificity). The same AI systems, linked with advanced Web technologies, present a highly scalable, transparent, and, above all, executable system incorporating explainable AI principles. The time taken to analyze data decreased markedly compared to manual interpretation while preserving clinically acceptable diagnostic reliability. Hence, time efficiency and diagnostic accuracy render the system clinically deployable.