Early Computer-Aided Diagnose in Medical Environments: A Deep Learning Based Lightweight Solution

MIGUEL NEHMAD ALCHE; DANIEL ACEVEDO; MARTA MEJAIL

ICT Applications for Smart Cities

Springer

Año: 2022; p. 149 - 164

The use of artificial intelligence in healthcare systems has helped doc-tors to automate many tasks and has avoided unnecessary patient hospitalizations.Mobile devices have grown in computing capacity and enhanced image acquisitioncapabilities, which enable the implementation of more powerful outpatient services.In this chapter we propose a lightweight solution to the diagnose of skin lesions.Specifically, we focus on the melanoma classification whose early diagnosis is cru-cial to increase the chances of its cure. Computer vision algorithms can be used toanalyze dermoscopic images of skin lesions and decide if these correspond to benignor malignant tumors. We propose a deep learning solution by means of the adaptationof the attention residual learning designed for ResNets to the EfficientNet networkswhich are suitable for mobile devices. A comparison is made of this mechanismwith other attention mechanisms that these networks already have incorporated. Wemaintain the efficiency of these networks since only one extra parameter per stageneeds to be trained. We also test several pre-processing methods that perform colorcorrections of skin images and sharpens its details improving the final performance.The proposed methodology can be extended to the early detection and recognitionof other forms of skin lesions.