Skin Disease Identification using CNN

In-depth research on the application of Convolutional Neural Networks to the automated classification of skin conditions is presented in this paper. The study uses CNN architectures, transfer learning, and data augmentation techniques to improve the classification accuracy of different skin conditions. When dealing with small datasets, transfer learning entails modifying previously trained models to categorize skin conditions. By adding more diversity to the training data, data augmentation enhances the model’s capacity to generalize and manage novel, unseen scenarios. The findings show the promise of CNN-based medical diagnostic systems, especially for the early identification of skin conditions. By achieving high accuracy in classification, our systems could provide significant support to healthcare professionals, reducing diagnostic errors and improving treatment outcomes. The findings underscore the value of CNNs in enhancing dermatological diagnostics and enabling scalable, automated solutions in medical care.