Medical Image Anomaly Triage Demonstrator (Explainable AI)
IEEE Published Document 10445174 aligned medical imaging anomaly classifier with interpretable neural network activations.
Project Overview
Advanced diagnostic support system utilizing attention mechanisms and Integrated Gradients to highlight pathological tissue anomalies in MRI and CT scans, providing transparent clinical justifications. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Medical Image Anomaly Triage Demonstrator (Explainable AI).
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.