eprintid: 20414
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/04/14
datestamp: 2026-01-12 12:18:10
lastmod: 2026-01-12 12:18:10
status_changed: 2026-01-12 12:18:10
type: conference_item
metadata_visibility: show
creators_name: Shen, Ng Yong
creators_name: Yi, Kh'Ng Xin
creators_name: Lin, Beh Woan
creators_name: Ooi, Boonyaik Yaik
title: Comparative Analysis of AI Models for Skin Type and Acne Severity Classification
ispublished: pub
keywords: Convolutional neural networks; Deep learning; Learning algorithms; Learning systems; Accurate analysis; Comparative analyzes; Convolutional neural network; Efficientnetb0; GPT assistant-based; Intelligence models; Resnet50; Skin analysis; Skin conditions; YOLOv8; Classification (of information)
note: Cited by: 0
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
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023668963&doi=10.1109%2FAiDAS67696.2025.11213898&partnerID=40&md5=d599ab2029f4d6c327e1d9bc700c945a
id_number: 10.1109/AiDAS67696.2025.11213898
full_text_status: none
pagerange: 455 - 460
refereed: TRUE
isbn: 9798331586034
citation:   Shen, Ng Yong and Yi, Kh'Ng Xin and Lin, Beh Woan and Ooi, Boonyaik Yaik  (2025) Comparative Analysis of AI Models for Skin Type and Acne Severity Classification.  In: UNSPECIFIED.