Transforming Dermatology: Transfer Learning Models for Accurate Skin Disease Detection

Abstract

This research investigates the application of machine learning techniques in the detection and classification of skin diseases. Leveraging transfer learning models such as MobileNetV2, InceptionV3, and DenseNet121, the study focuses on accurately identifying nine distinct skin disease categories using a curated dataset. The methodology encompasses data preprocessing, including normalization and augmentation, to mitigate class imbalance and enhance model performance. DenseNet121 emerged as the most effective model, achieving an accuracy of 86.2%, followed by MobileNetV2 and InceptionV3. The study highlights the challenges of dataset limitations, interpretability of models, and computational resource requirements. Ethical considerations, including data privacy and bias mitigation, are addressed to ensure responsible implementation. This research demonstrates the feasibility of deploying AI-driven diagnostic tools to augment dermatological care, emphasizing the potential for widespread application in remote and resource-limited settings. Future work involves expanding datasets, improving model interpretability, and integrating these solutions into telemedicine platforms for more accessible and equitable healthcare.