Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis - Duman, Erkan; Tolan, Zafer (Wiley, 2023)
If you decided to utilize deep learning in any image processing application, you would be faced with the issue, Which architecture should I use? due to the proliferation of existing CNN models and their advancements. Unfortunately, your answer will only be partially correct because each alternative has its advantage. The underlying idea of this research is to combine recent CNN models instead of selecting just one for optimal accuracy. Our study applied this idea to color lesion images to diagnose skin diseases. By ensembling, the recent CNNs, over 99% classification accuracy and over 97% sensitivity were achieved for the ISIC-2017 dataset, which contains 2000 lesion images. Our mean sensitivity and AUC values for classifying 10000 color lesion images into seven different skin diseases (ISIC-2018) were 0.825% and 0.922%, respectively. In categorizing over 25000 images from the ISIC 2019 dataset, our suggested technique achieved a mean sensitivity of over 90%.
Keywords: deep Learning; dermoscopic images; ensembling CNNs; skin diseases diagnosis