ADVANCED CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATED SKIN DISEASE DETECTION: A COMPREHENSIVE AI-DRIVEN APPROACH
Abstract
This study presents the development and application of an advanced AI framework for automated skin disease detection using Convolutional Neural Networks (CNNs). By integrating pre-trained models with diverse dermatological datasets, including the HAM10000 dataset, the framework aims to enhance diagnostic accuracy and reliability. The research details the mathematical foundation of CNNs, the fine-tuning of pre-trained architectures, and the application of state-of-the-art AI techniques such as data augmentation and cross-validation. The results demonstrate a significant improvement over existing models, with the framework showing high potential for clinical application in dermatology.
Keywords
Convolutional Neural Networks (CNNs), Skin Disease Detection, Dermatology AI, HAM10000 Dataset, Inception v3 Architecture, Deep Learning, Automated Diagnosis, Medical Image Classification, Data Augmentation, Transfer Learning.
References
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