SCALP CONVICAP: AN INTELLIGENT CONVINEXT FRAMEWORK FOR HAIR AND SCALP DISEASE DETECTION USING HYBRID CONVNEXT TRANSFORMER LEARNING WITH GRAPH AND CAPSULE NETWORKS

Abstract

Hair and scalp diseases such as alopecia areata, psoriasis, seborrheic dermatitis, and folliculitis affect millions globally and require accurate diagnostic assessment. This research presents SCALP CONVICAP, an intelligent computer-aided diagnostic framework for dermatological imaging. The architecture combines ConvNeXt with Graph Neural Networks and Capsule Networks to capture spatial hierarchical relationships and textural scalp variations. Dermatoscopic scalp images undergo illumination correction and hair artifact removal before deep feature representation. Benchmarked against clinical images, the proposed framework achieves an accuracy of 97.1%, showcasing substantial improvement over standard CNN baselines.