Skin diseases can greatly impact an individual's physical and psychological well-being. Early and proper diagnosis is necessary for different skin diseases to be treated and controlled effectively. Skin diseases, especially melanoma, basal cell carcinoma and toxic epidermal necrolysis (TEN), are harmful and highly infectious. If caught early, these skin conditions can be cured. The main issue with this is that only skilled dermatologists can identify and categorize these conditions. One kind of machine learning called deep learning enables systems to learn facts and understand the world through a hierarchy of concepts. The skin disease detection tool uses deep learning, machine learning and image processing methods. The suggested instruments are accurate, simple to use and non-invasive for determining the right skin conditions.

CNN, ANN, AlexNet, VGGNet, ResNet, DenseNet, Deep Learning

Deep learning has transformed dermatology by enabling rapid and accurate skin condition diagnosis. To realize its full potential, despite the significant advancements, problems like generalizability, model interpretability and dataset diversity still need to be fixed. Deep learning has revolutionized dermatological diagnosis by providing enhanced diagnostic capabilities. Even with remarkable progress, there is still room for improvement, particularly in the areas of dataset diversity and algorithm accuracy. The combination of results from recent and earlier studies emphasizes how important deep learning is to improving the diagnosis of skin diseases. For these technologies to be dependable and successful in actual clinical settings, improved model interpretability, multimodal data approaches and the integration of various datasets are essential. Therefore, future studies should try to close these gaps and hasten the adoption of sophisticated computational techniques in routine dermatological practice.

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