• Version
• Download 10
• File Size 774.67 KB
• File Count 1
• Create Date 11 June 2025
• Last Updated 11 June 2025

DEEP LEARNING-BASED DIAGNOSIS FOR SKIN DISEASE PREDICTION

Authors:

P.LOGAIYAN ¹, L.DEEPIKA ²

¹ Associate Professor, Department of MCA, Sri Manakula Vinayagar Engineering College, Puducherry-605107, India

2. ². PG Student, Department of MCA, Sri Manakula Vinayagar Engineering Collesge, Puducherry-605107 India

 deepit0114@gmail.com

Abstract- Skin diseases are increasingly prevalent, and their accurate diagnosis remains challenging due to the complexity of medical imaging and the diversity of conditions. Existing systems primarily rely on Generative Adversarial Networks (GANs) for skin disease prediction, but they face stability issues and inconsistencies during training. Additionally, the reliance on synthetic data generation often results in less accurate predictions when the generated images fail to fully capture real-world variability. To overcome these limitations, the proposed system employs EfficientNetB0, a deep learning model optimized for medical image analysis. EfficientNetB0 uses a compound scaling method to balance depth, width, and resolution, enabling efficient feature extraction while maintaining high accuracy. Its lightweight architecture allows for faster processing without compromising performance, making it ideal for skin disease classification. By leveraging EfficientNetB0, the system enhances early detection, improves diagnostic Precision, and reduces misclassification risks, ultimately supporting better patient outcomes in clinical practice.

Keywords: Skin disease diagnosis, EfficientNetB0, deep learning, medical imaging, feature extraction, classification, prediction accuracy, healthcare AI, early detection, diagnostic precision.