Development of an accurate AI-based dermatology assistant for skin disease recognition using YOLOv8 models

Huzaini, Muhammad Irfan Darwish and Mansor, Hasmah and Gunawan, Teddy Surya and Ahmad, Izanoordina (2024) Development of an accurate AI-based dermatology assistant for skin disease recognition using YOLOv8 models. In: 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 30-31 July 2024, Bandung, Indonesia.

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Abstract

The AI-based dermatology assistant for skin disease recognition is a state-of-the-art technological solution designed to address the disparity in access to professional dermatological services between urban and remote areas. This research employs the YOLOv8 model, which is a deep learning algorithm, to determine the most effective methods for detecting skin diseases. The research compares various algorithms, such as SVM, YOLOv3, YOLOv4, and Dual-Architecture CNN, through a comprehensive review of existing AI applications in dermatology. After 500 training epochs, the YOLOv8 Small Model was the most accurate, achieving a precision of 84%, a recall of 77.1%, and a mean average precision (mAP) of 84%. The potential of the proposed AI-based assistant to significantly improve healthcare accessibility and diagnostic accuracy in underserved areas is demonstrated through rigorous testing, which validates its effectiveness. This groundbreaking utilization of artificial intelligence in dermatology signifies a substantial stride forward in providing equitable healthcare solutions.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	Artificial Intelligence, Dermatology, Skin Disease Recognition, YOLOv8, Deep Learning
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr Hasmah Mansor
Date Deposited:	20 Sep 2024 11:28
Last Modified:	20 Sep 2024 11:28
URI:	http://irep.iium.edu.my/id/eprint/114525

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