ADVANCING DIABETIC FOOT ULCER DETECTION BASED ON RESNET AND GAN INTEGRATION

Abstract

Diabetes, characterized by the body's inability to effectively regulate sugar levels due to insulin complications, leads to various serious health issues. Among these, Diabetic Foot Ulcer stands out as a critical yet often ignored consequence. This condition, if not addressed in time, can result in severe outcomes including amputations, posing a substantial burden on both individuals and healthcare systems, particularly in areas where medical care is costly. Addressing this pressing issue, our research focused intensively on the analysis of medical images, with the goal of enhancing the accuracy of Diabetic Foot Ulcer diagnosis. We assessed two different models: the renowned ResNet50 model and hybrid model that fuses ResNet50 with Generative Adversarial Networks. The findings were noteworthy; the ResNet50 demonstrated commendable performance, achieving an average accuracy and precision of 0.76, and an F1-Score of 0.75. However, the hybrid model surpassed these metrics, registering an average accuracy of 0.84, precision of 0.85, and an F1-Score of 0.84. This research contributes to the evolving landscape of medical image analysis, offering a promising avenue for more precise and effective DFU diagnosis in clinical settings. The marked advancement in diagnostic precision afforded by the hybrid model suggests a significant stride forward in effectively managing and treating DFU.