Automated Facial Skin Disease Classification via Deep Learning: Enhancing Diagnostic Accuracy - Pranitha, Sekhar; Babu, K Mahesh; Priyanka, P M; Prasanna, Kota Lakshmi; Tabita, Satri; Chandrika P, Raga (Journal of Neonatal Surgery, 2025)
Facial skin conditions present major obstacles to public health, requiring precise and prompt diagnosis for successful treatment. This study introduces a technique utilizing ResNet50 V2 based on deep learning to categorize different types of facial skin conditions. Utilizing a thorough dataset and implementing advanced image processing methods like data augmentation and normalization results in the model attaining high accuracy and robustness. ResNet50 V2 shows better precision, recall, and efficiency than traditional classification methods. Our method is verified by comprehensive experiments that demonstrate encouraging outcomes in the early identification and treatment of skin ailments. This advanced deep learning model provides a valuable tool for healthcare professionals, improving diagnostic accuracy and enhancing patient care outcomes. The study adds to the field of medical image analysis by offering a scalable and efficient method for classifying diseases.