A Survey On Intelligent Skin Disease Prediction Using Deep Learning And Cnn–llm Integration

Abstract

Skin Diseases Are Among The Most Prevalent Medical Issues Globally, Making Timely And Accurate Diagnosis Essential For Successful Treatment.Expert Clinical Evaluation Is The Traditional Foundation Of Dermatological Diagnosis.However, This Approach Is Limited By Subjectivity, Accessibility, And Resource Availability. Automated Skin Disease Prediction From Medical Images Is Now Feasible Thanks To Recent Advances In Artificial Intelligence (AI) And Deep Learning. This Survey Comprehensively Examines Current Methods For Identifying Skin Diseases, Leveraging Machine Learning And Deep Learning. Specifically, It Reviews The Application Of Convolutional Neural Networks (CNNs),transfer Learning Models, And Integrated Hybrid AI Frameworks.The Analysis Covers The Strengths And Weaknesses Of Popular Classification Methods, Along With An Investigation Into Difficulties Like Imbalanced Datasets Lack Of Clarity, Poor Interpretability, And Problems With Generalization. This Survey's Core Contribution Is The Examination Of Integrated Frameworks That Combine CNN-based Image Classification With Large Language Models (LLMs). This Integration Facilitates The Delivery Of Explainable Diagnostic Insights, Stage Analysis, And Informed Treatment Recommendations. These Systems Bridge The Gap Between Automated Prediction And Clinical Decision Support By Integrating Visual Recognition With Semantic Understanding

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