A CNN-Based Review of Artificial Intelligence in Dermatology of Skin Disease Diagnosis
Keywords:
Convolutional Neural Networks (CNN), Deep Learning In Dermatology, AI-Based Skin Disease Detection, Transfer Learning Models, Medical Image ClassificationAbstract
Early skin disease identification and categorization have been transformed by deep learning and artificial intelligence (AI) applications in dermatology. CNNs, or convolutional neural networks, are a potent automated diagnostic tool that performs more accurately and efficiently than traditional methods. This research looks at the advancements in CNN-based skin disease diagnosis. with particular attention paid to transfer learning models like DenseNet121, ResNet50, ResNet18, and VGG16. It critically evaluates previous studies, emphasizing methodological strategies, dataset usage, performance indicators, and research needs. The paper also covers data augmentation, segmentation-free classification, and practical clinical applications, as well as the difficulties and potential paths in AI-driven dermatological diagnosis. This study attempts to give a thorough overview of AI-driven skin disease identification and its potential to revolutionize dermatological treatment by combining the most recent advancements
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