• P B V Ashritha
• P Suneetha
• Ch Aparna
• Ch Adithi
• K Sowmya Sree

The advanced Convolutional Neural Network(CNN) model is developed to excel a dual pupose: the diagnosis of diverse facial skin diseases and basic required prescription. By leveraging transfer learning, the model optimizes the extraction of features, demonstrating high accuracy in identifying a wide range of skin conditions. A specialized Convolutional layer enhances skin disease prediction based on facial features, while ensemble techniques contribute to robust diagnostic insights, establishing the model as a reliable tool for dermatological assessments. The model’s performance is rigorously validated using evaluation metrics such as accuracy for disease diagnosis and Mean Absolute Error for estimation across various datasets. This research represents a significant stride in automated dermatological diagnostics, providing an efficient approach for concurrent evaluation of skin health and basic required prescription. The proposed model showcases substantial potential for clinical applications, offering a valuable contribution to personalized healthcare and the evolution of machine learning in dermatology. This innovative approach is composed to redefine diagnostic practices, escorting in a new era of precision and efficiency in dermatological health assessments.

"Integrated CNN Model for Facial Skin Diagnostics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i6-i12, April-2024, Available :http://www.jetir.org/papers/JETIR2404802.pdf

"Integrated CNN Model for Facial Skin Diagnostics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi6-i12, April-2024, Available at : http://www.jetir.org/papers/JETIR2404802.pdf