Cuckoo Search-Optimized Residual Networks for Identification of Diabetic Foot Ulcers

The identification of Diabetic foot ulcers (DFUs) is a complicated medical diagnosis and treatment that is tough to deal with, so it needs to be very accurate and effective. The purpose of the study is to show the way to better recognize the DFUs through the use of Cuckoo Search-Optimized Residual Networks (CS-ResNet). The CSO’s contribution to the network allows it to adapt to the learning process, thus enhancing feature extraction and classification performance. The approach is based on the optimization strategy, which is soft tuning of the deep learning model’s parameters that, in turn, makes it more efficient in finding the right solutions and in achieving the best results in the identification of DFUs. The residual network framework is the cause of gradient vanishing issues because it provides a deep enough representation learning for complex ulcer patterns. The said model contains a feature selection mechanism that constantly improves the accuracy of detection through the selection of optimized features. The hybrid approach that includes both Cuckoo Search and Cuckoo Search Optimization strengthens model stability, which in turn makes medical imaging real-world applications more confirmable. The pilot study, which aimed to track the predicted enhanced DFU identification with the CS-ResNet model, shows the high efficacy of the method.