A Hybrid CNN-Rule-Based Classifier Skin Infection Determination Model

• Author(s): Ezukuse B.G.; Akazue, M.I.
• Paper ID: 1715314
• Page: 1887-1907
• Published Date: 24-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026

Abstract

Skin infections are affecting millions of individuals across all age groups and significantly impacting quality of life because of climate change and other factors. Skin infections can be on any part of the human body. It usually occurs on the back head, Lip, foot, lap, leg, hand, and so on. These infections can be discomforting and embarrassing to the individual. Therefore, early detection is crucial for effective treatment, inhibition of disease development, and minimizing psychosocial effects. This paper evaluates the use of different machine learning techniques by different authors by using document review and site visit. The results obtained, methodology applied, system analysis and design tools were the factors and metrics used for evaluation. It concludes that a hybridized CNN Rule-based classifier is a better model for skin infection determination in the developing countries. The paper recommends the implementation of this model for healthcare givers.

Citations

IRE Journals:
Ezukuse B.G., Akazue, M.I. "A Hybrid CNN-Rule-Based Classifier Skin Infection Determination Model" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1887-1907

IEEE:
Ezukuse B.G., Akazue, M.I. "A Hybrid CNN-Rule-Based Classifier Skin Infection Determination Model" Iconic Research And Engineering Journals, 9(9)