Christian J Lopez, Daniel Santa Mina, Victoria Tan, Manjula Maganti, Cheryl Pritlove, Lori J Bernstein, David M Langelier, Eugene Chang, Jennifer M Jones
CaRE@ELLICSR: Effects of a clinically integrated, group-based, multidimensional cancer rehabilitation program
Cancer medicine
diabetic foot ulcer dfu poses significant threat individuals affected diabetes often leading limb amputation early detection dfu greatly improve chances survival diabetic patients work introduces fusionnet novel multiscale feature fusion network designed accurately differentiate dfu skin healthy skin using multiple pretrained convolutional neural network cnn algorithms dataset comprising 6963 skin images 3574 healthy 3389 ulcer various patients divided training 6080 images validation 672 images testing 211 images sets initially three image preprocessing techniques gaussian filter median filter motion blur estimation applied eliminate irrelevant noisy blurry data subsequently three pretrained cnn algorithms densenet201 vgg19 nasnetmobile utilized extract highfrequency features input images features inputted metatuner module predict dfu selecting discriminative features statistical tests including friedman analysis variance anova employed identify significant differences fusionnet subnetworks finally three explainable artificial intelligence xai algorithms shap shapley additive explanations lime local interpretable modelagnostic explanations gradcam gradientweighted class activation mapping integrated fusionnet enhance transparency explainability fusionnet classifier achieved exceptional classification results 9905 accuracy 9818 recall 10000 precision 9909 auc 9908 f1 score believe proposed fusionnet valuable tool medical field distinguish dfu healthy skin