Dermatological Disease Detection Using CNN

Skin diseases are mild infections to serious diseases such as melanoma, and it is necessary to diagnose them at an early stage for proper treatment. This paper proposes a Convolutional Neural Network (CNN)-based classifier to identify skin diseases from images automatically. The model was trained on a huge dermatological database with data augmentation, transfer learning, and hyperparameter optimization. Deep learning architectures like ResNet, EfficientNet, and DenseNet were explored to achieve high accuracy. Testing revealed the model was as accurate as 95%, outperforming conventional diagnostic techniques. The system helps dermatologists by enhancing diagnostic accuracy, minimizing manual labor, and facilitating quicker decision-making. Through the incorporation of artificial intelligence in dermatology, this study improves medical imaging, leading to improved patient outcomes and health efficiency.