Accelerated Deep Learning Framework With Hybrid Techniques for Hair Disease Diagnosis and Telemedicine Integration

Background information: Alopecia and seborrheic dermatitis are two dermatoses that lower quality of life. The current diagnoses are subjective and time-consuming. EfficientNet-B3 and CBAM, two deep learning-based telemedicine tools, offer scalable, accurate solutions to the issues of data scarcity and class imbalance. Objectives: The hybrid deep learning system, by combining EfficientNet-B3, CBAM, and GANs, will address class imbalance and provide a simpler platform for telemedicine-based accurate, rapid, and scalable hair illness diagnosis. Methods: Harvesting of features is performed by using EfficientNet-B3 and CBAM. GANs are applied for rectification of the synthetic data class imbalance problem. For the purpose of achieving light scaling, knowledge distillation is used, while preprocessing, augmentation, and RAdam are utilized for the optimization of training. Results: With a delay of 15 ms, 99.5% accuracy and 98.9% precision were achieved. Conclusion: In conclusion, it helps underprivileged communities and ensures real-time scalability with accuracy in diagnosis.