Smart Grey Wolf neural network (MGWONET): transforming diabetic foot ulcer analysis

Diabetes frequently results in diabetic foot ulcers (DFUs), which can lead to lower limb amputation if left untreated. Current DFU management consists of a multidisciplinary team approach, including physicians, podiatrists, wound care specialists, nursing staff and patients. Traditional diagnoses of DFUs can be expensive, lengthy, and generally reliant on local and private clinical evaluation. There is a need for an automated, remote, diagnostic option for patients with suspected DFUs.

This paper introduces MGWONET, a new model for deep learning (DL) on classification problems, using the Modified Grey Wolf Optimisation (MGWO) algorithm to automatically find the optimal configuration of hyperparameters. The automation settings are intended to govern the model's operation, and include the number of layers, learning rate and filter sizes. To improve the accuracy and efficiency of DL models, it is essential to carefully select the right hyperparameters. However, choosing the best combination involves searching through a large number of possible settings, which is computationally challenging and complex. Unlike manual tuning, which is time-consuming and inefficient, the MGWO algorithm efficiently explores the large and complex hyperparameter search space to improve the model's accuracy and robustness. MGWONET is intended for classifying skin patches as healthy or ulcerated, and was trained with an augmented dataset of 2200 DFU images. Generally recognised metrics, such as accuracy, recall, precision, specificity, balanced classification rate and the receiver operating characteristic curve were used to evaluate optimal model performance.

The optimal model had a classification accuracy equating to 98.64% and was superior to a selection of well-known DL architectures: AlexNet; VGG16; GoogLeNet; and a monochrome baseline Grey Wolf optimised model. This study is centred on binary image-based classification and does not constitute a clinical grading system; however, the method presented here has the potential to be a valuable supportive addition to clinical practice.

The MGWONET framework has proved to be highly reliable and provides robust discriminative power, making it a strong candidate for automated DFU diagnosis. It has a potential role in supporting clinicians, reducing diagnostic burdens, and accessing early urgent interventions through smart health services.