Skin Disease Detection Using CNN: 6th International Conference on Innovative Trends in Information Technology(ICITIIT-2025)

Skin diseases and it's infectious diseases are the most common health issues, requiring quick and correct diagnosis for appropriate treatment. In this study to describe the uses of convolutional neural networks (CNN) to recognize and classify different skin conditions, we use 12 review papers. We give an idea of CNN's basic ideas and highlight their benefits over more conventional image processing methods. The system development should be improved and detailed by the recent developments in dataset construction, preprocessing methods, and augmentation tactics. We evaluate the positive and negative values as well as the accuracy of some cutting-edge CNN models used in skin disease detection. The difficulties of applying clinical contexts of CNN as well as the concerns about the data privacy interpretability of the system models and the requirement of the dataset training should be shown in this paper. The challenges of using CNNs in clinical settings are also discussed including issues with data privacy, model interpretability, and the need for trustworthy training datasets. We wrap up by describing possible research directions and emphasizing how integrating CNNs with telemedicine and wearable technology could improve access to dermatological care. This review provides a wealth of information for researchers and practitioners interested in applying deep learning techniques in dermatology.