Detection of Diabetes Mellitus With Deep Learning and Data Augmentation Techniques on Foot Thermography

Diabetes mellitus (DM) is a metabolic disorder characterized by increased blood glucose. The pathology can manifest itself with different conditions, including neuropathy, the main consequence of diabetic disease. Statistics show worrying figures worldwide, diagnosed an estimated 1.6 million people with DM by 2025. In this sense, alternative and automated methods are necessary to detect DM, allowing it to take the pertinent measures in its treatment and avoid critical complications, such as the diabetic foot. On the other hand, foot thermography is a promising tool that allows visualization of thermal patterns, patterns that are altered as a consequence of shear and friction associated with lower limb neuropathy. Based on these considerations, we explored different strategies to detect patients with DM from foot thermography in this research. Initially, the study focused on finding a classification index like Thermal Change Index (TCI). Subsequently, we used the deep convolutional neural networks paradigm, implementing 12 different data augmentation methods, of which four are conventional, and 8 are newly proposed methods. The results showed that the proposed and the conventional methods increased the network's performance, where a 100% detection was achieved by weighting the DM probability percentages for both images of the feet. Finally, it was also possible to demonstrate the importance of transfer learning, which does not depend on the type of database, but on the data corpus with which the transfer was trained.