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primary topic: Comparative Analysis of AI Models for Skin Type and Acne Severity Classification

Comparative Analysis of AI Models for Skin Type and Acne Severity Classification
type: AcademicArticle
type: Article
abstract: Skin acne is a common problem faced by many people worldwide, which will affect both confidence and physical appearance of individuals. Accurate analysis of skin condition is essential to ensure that users can understand their skin condition. Recently, artificial intelligence (AI) has demonstrated strong potential in overcoming these challenges by delivering accurate results. In the field of AI, techniques like machine learning and deep learning are increasingly applied to the analysis of skin conditions. While deep learning has shown strong potential, its accuracy remains limited in certain scenarios. To address this, the project aims to identify the most effective skin analysis model among Classic CNN, ResNet50, EfficientNetB0, and YOLOv8. These models are evaluated based on their ability to accurately assess users' skin type and acne severity. Furthermore, the integration of GPT-4o's natural language processing capabilities introduces a novel GPT assistant-based approach. This complements traditional deep learning methods by enhancing interpretability and flexibility in prediction tasks, thereby offering a more comprehensive and user-friendly skin analysis solution. © 2025 IEEE.
date: 2025
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

Ng Yong Shen
type: Person
given name: Ng Yong
family name: Shen

Boonyaik Yaik Ooi
type: Person
given name: Boonyaik Yaik
family name: Ooi

Beh Woan Lin
type: Person
given name: Beh Woan
family name: Lin

Kh'Ng Xin Yi
type: Person
given name: Kh'Ng Xin
family name: Yi

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primary topic: Comparative Analysis of AI Models for Skin Type and Acne Severity Classification