Diabetic Foot Ulcer Classification Using Sequential CNN Algorithm

The occurrence of diabetic foot ulcers (DFUs) and their possible consequences provide a major healthcare challenge. For prompt care and the avoidance of serious problems, early and accurate detection of DFUs is essential. This study proposes a novel approach for automatic DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful deep learning technique proven effective in image analysis. A large dataset of foot photos covering a variety of DFU types, phases, and circumstances is used to train the suggested CNN model. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection.The integration of this technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. This research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management

Algorithm · Diabetes mellitus · Diabetic foot · Diabetic foot ulcer · Artificial Intelligence in Healthcare · Computer Science · Diabetic Foot Ulcer Assessment and Management · Digital Imaging for Blood Diseases · Medicine · Artificial Intelligence

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  Open Access•Rene Markovič, Vladimir Grubelnik et al.•Frontiers in Public Health•2023

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