Detection of Diabetes Mellitus with Deep Learning and Data Augmentation Techniques on Foot Thermography
Keywords:
Diabetic Foot Ulcer, DFU, deep learning, data augmentation, foot thermography, detection, Vision Transformer, VIT, EfficientNet, classification, machine learning, image analysis, healthcare, diagnosis, limb amputations, patient outcomesAbstract
Diabetic foot ulcers (DFUs) are a major global health issue with heavy burdens on patients and the healthcare systems. They can be potentially used by an automatic detection tool to assist specialists in early diagnosis and treatment and in preventing much severe consequences like amputation. The study attempts at examining deep learning with data augmentation for the detection of DFUs from thermographic foot images. The use of advanced models such as Vision Transformer (VIT) and EfficientNet aims to improve accuracy and efficiency in detection relative to traditional machine learning algorithms. The result is intended to help determine the difference between DFUs and healthy foot conditions with confidence in supporting early intervention and enhanced outcome for patients.
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