Computer Aided Diagnoses System for Multi Skin Disease Classification using Deep Learning Techniques

Abstract

Skin cancer is a common ailment that arises predominantly from regular exposure to sunlight and manifests across various areas of the body. The accurate recognition and Classification of skin lesions present considerable challenges due to their morphological diversity and the oftensimilar features between different types of skin malignancies. In current times, the application of deep learning methodologies in image-based diagnosis of skin lesions has shown promising results, achieving levels of diagnostic accuracy comparable to those of professional dermatologists. Researchers developed an automated system that uses Artificial Intelligence (AI) and deep learning models to enable early detection of skin disease. This research collected skin disease photographs from recognized online sources. A new framework called Skin-D was then developed to assess different kinds of skin conditions. To create this model, we combined MobileNet architecture, residual blocks and dense blocks with a transition layer in a deep neural network. The Skin-D algorithm is trained using a dataset of 79,665 skin pictures. The Skin-D classification approach achieved 99%, 98.5%, 97.5% and 89% accuracy on four separate datasets. These findings indicate that the model delivers positive results and might be utilized by healthcare practitioners as a diagnostic tool. In terms of accuracy, the Skin-D approach outperformed the cutting-edge models SkinLesNet and MobileNet V2-LSTM.