Resumen
The very good results achieved with recent algorithms for image classification based on deep learning have enabled new applications in many domains. The medical field is one that can greatly benefit from these algorithms in order to help the medical professional elaborate on his/her diagnostic. In particular, portable devices for medical image classification are useful in scenarios where a full analysis system is not an option or is difficult to obtain. Algorithms based on deep learning models are computationally demanding; therefore, it is difficult to run them in low-cost devices with a low energy consumption and high efficiency. In this paper, a low-cost system is proposed to classify skin cancer images. Two approaches were followed to achieve a fast and accurate system. At the algorithmic level, a cascade inference technique was considered, where two models were used for inference. At the architectural level, the deep learning processing unit from Vitis-AI was considered in order to design very efficient accelerators in FPGA. The dual model was trained and implemented for skin cancer detection in a ZYNQ UltraScale+ MPSoC ZCU104 evaluation kit with a ZU7EV device. The core was integrated in a full system-on-chip solution and tested with the HAM10000 dataset. It achieves a performance of 13.5 FPS with an accuracy of 87%, with only 33k LUTs, 80 DSPs, 70 BRAMs and 1 URAM.