Automated DFU detection through GA-selected CNN ensemble with Grad-CAM interpretability

Abstract

Diabetic foot ulcers (DFU) are a highly dangerous and even fatal sequela of diabetes mellitus, very often becoming a lower limb amputation and imposing significant morbidity and mortality all over the world. Deep learning and machine learning have recently made breakthroughs, creating potential for being applied in medical image analysis towards the detection of DFU, but the field still faces the challenge of optimizing the performance of the models in both terms of accuracy and interpretability. The present paper solves this issue by introducing a more optimized deep learning model to detect DFUs that has been proposed by integrating a proprietary Convolutional Neural Network (CNN) structure and a Genetic Algorithm (GA) based on ensembles. Seven different optimizers were distinctly trained by the custom CNN. The genetic algorithm (GA) was then applied to pool a final ensemble model out of individual models with the most remarkable performance. This GA based ensemble had an accuracy of 97%, a precision of 95%, a recall of 99%, and an F1 score of 97%, which showed better and stronger performance than the individual single models that made up it. In addition, the study implemented Grad-CAM (Gradient-weighted Class Activation Mapping) to increase interpretation and transparency. The results obtained demonstrated that Grad-CAM was able to accurately describe the features associated with ulcers and, therefore, offers invaluable information on how the model makes its decisions to healthcare professionals.