Deep Learning Model For Scalp Disease Prediction From Multimodal Framework

Abstract

Scalp Disorders Such As Dandruff, Folliculitis, And Alopecia Are Prevalent Conditions Impacting Millions Worldwide. Accurate Detection Of Scalp Diseases Through Deep Learning Can Significantly Improve Diagnostic Efficiency And Treatment Outcomes. This Paper Presents A Multimodal Deep Learning Approach That Integrates Convolutional Neural Networks (CNN) And Recurrent Neural Networks (RNN) To Analyze Scalp Images And Corresponding Symptom Descriptions. The Combined Framework Enhances Interpretability And Accuracy Over Traditional Single-modality Systems. Results Demonstrate That The Proposed System Achieves Robust Prediction Accuracy Across Multiple Scalp Disease Categories.

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