Diabetic foot ulcer classification using an enhanced coordinate attention integrated ConvNext model

Diabetic foot ulcers (DFUs) pose a significant complication of diabetes with the potential to lead to amputation if not effectively managed. Current DFU treatments require rigorous monitoring by both healthcare professionals and patients, which is challenging due to the high costs associated with diagnosis, treatment and long-term care. A major limitation of these approaches is their limited capacity to identify highly relevant pattern connections and broad contextual correlations resulting inaccuracies in classifying regions of interest. This research introduces an attention enhanced deep learning-based automated approach for assessing DFUs using images to expedite the investigation process and offer optimal recommendations. Adaptive thresholding is employed to enhance the contrast and uniformity of DFU images and thereby improves the feature extraction. A hybrid model incorporating coordinate attention enhanced ConvNeXt is used for effective DFU image classification to enhance the representation of complex patterns through efficient parameter utilization. The ConvNeXt architecture is designed to scale efficiently across various sizes by utilizing depthwise separable convolutions and improved image normalization. This model is augmented with coordinate attention, which captures spatial information in both horizontal and vertical directions, aiding in the extraction of long-range dependency features for more accurate classification of DFU images. Experimental results demonstrate that the model achieves an accuracy of 97.16% and F1-score of 0.97.