  The Internet of Medical Things (IoMT) has dramatically benefited medical
professionals that patients and physicians can access from all regions.
Although the automatic detection and prediction of diseases such as melanoma
and leukemia is still being researched and studied in IoMT, existing approaches
are not able to achieve a high degree of efficiency. Thus, with a new approach
that provides better results, patients would access the adequate treatments
earlier and the death rate would be reduced. Therefore, this paper introduces
an IoMT proposal for medical images classification that may be used anywhere,
i.e. it is an ubiquitous approach. It was design in two stages: first, we
employ a Transfer Learning (TL)-based method for feature extraction, which is
carried out using MobileNetV3; second, we use the Chaos Game Optimization (CGO)
for feature selection, with the aim of excluding unnecessary features and
improving the performance, which is key in IoMT. Our methodology was evaluated
using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results
indicated that the proposed approach obtained an accuracy of 88.39% on
ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell. Moreover, our approach had
successful performances for the metrics employed compared to other existing
methods.
