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 MobileNet V1
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.