Performance Evaluation of Embedded Image Classification Models Using Edge Impulse for Application on Medical Images

This work explores the possibility of applying edge machine learning technology in the context of portable medical image diagnostic systems. This was done by evaluating the performance of two machine learning (ML) algorithms, that are widely used on medical images, embedding them into a resource-constraint Nordic nrf52840 microcontroller. The first model was based on transfer learning of the MobileNetVI architecture. The second was based on a convolutional neural network (CNN) with three layers. The Edge Impulse platform was used for training and deploying the embedded machine learning algorithms. The models were deployed as a C++ library for both, a 32-bit floating point representation and an 8-bit fixed integer representation. The inference on the microcontroller was evaluated under four different cases each, using the Edge Impulse EON compiler in one case, and the Tensor Flow Lite (TFLite) interpreter in the second. Results reported include the memory footprint (RAM, and Flash), classification accuracy, time for inference, and power consumption.