Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things
It addresses the problem of inefficient disease detection in IoMT for medical professionals and patients, with incremental improvements in accuracy.
This paper tackles medical image classification for diseases like melanoma and leukemia in the Internet of Medical Things (IoMT) by combining transfer learning with MobileNetV3 and Chaos Game Optimization for feature selection, achieving accuracies of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-Cell datasets.
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.