Work

Reviewing usage of DL models and ML Classifiers in Identification of Scalp Related Maladies

Year: 2026

Type: conference-paper

Abstract: Scalp conditions such as psoriasis, atopic dermatitis, tinea capitis, dandruff and alopecia are often encountered by individuals. Descriptive studies have provided evidence indicating that the condition of the scalp has a detrimental effect on hair quality. The efficacy of Convolutional Neural Networks (CNN) combined with Fully Convolutional Networks (FCN) in the diagnosis of scalp and skin problems has been substantiated by recent advancements in Deep Learning. In addition to the aforementioned investigation, a machine learning (ML) based method is utilized in this paper. The SVM and KNN algorithms achieved accuracies of 92.56% and 88.44% respectively in correctly classifying alopecia areata and the fit scalp. The utilization of deep learning models aimed at the analysis of scalp-related illnesses has witnessed significant enhancements owing to breakthroughs in computer vision and computational capabilities. However, there exists a vast scope for additional developments in this domain.

Source: International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI)

DOI: 10.1109/icscai68849.2026.11649006

Authors: Jitesh Mahant, Pankaj Tiwari

Institution: Kalinga University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7203665635