This research presents three neural network models tailored for medical diagnostics, each focused on a distinctive clinical application. The first model employs transfer learning using the Inception-ResNet-v2 architecture to classify COVID-19 and pneumonia using chest X-ray images. The second model utilizes the EfficientNetB7 architecture with transfer learning via fine-tuning to detect various stages of dementia from the MRI brain scan images. The third and central contribution features an ensemble of five WiSARD models - a weightless neural Network (WNN) architecture for arrhythmia classification by leveraging the MIT-BIH dataset, following the AAMI standards.
To improve prediction reliability in ambiguous scenarios, the WiSARD model incorporates a bleaching mechanism that incrementally resolves classification ties. After developing and validating the ensemble WiSARD model within a PyTorch-based software framework, it is translated into synthesizable hardware description language (HDL). The resulting hardware model is then used for power and area analysis across 45 nm and 90 nm technology nodes.
By merging neural network accuracy with hardware efficiency, this research offers a practical and scalable solution for edge-based medical diagnostics. The WiSARD model’s memory-efficient, enhanced ensemble voting and bleaching logic offer an optimal trade-off between power and chip area. Together, these three models demonstrate a tailored AI and hardware co-design approach to enable real-time, on-device medical inference, advancing an intelligent healthcare system that supports timely and reliable patient monitoring.Electrical and Computer Engineerin
