Identification Of Different Chronic Skin Conditions Using Deep Convolutional Neural Networks

Human skin disease has recently emerged as a widespread condition, with millions of individuals afflicted with diverse skin conditions. Most often, these problems have concealed risks that put individuals prone to getting skin cancer. Because of the low standard of visual images of skin infections, treating these various illnesses requires highly skilled medical professionals and sophisticated machinery. Additionally, manual skin diagnosis is time-consuming, tedious, and demands additional human effort. In most prior studies on skin disease, CNN is utilized for making conventional loss functions, which limited the model’s ability to learn distinctive features from skin images. So, the Resnet152 and InceptionResNet-V2 layers are fine-tuned using a loss function, ensemble, and MobileNet models we proposed a new framework to solve the issue mentioned above. The performance of the suggested framework, which achieves superior accuracy in skin disease tasks than many existing works, is demonstrated by experiment results and their analysis.