Ringworm Detection and Diagnosis System.

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dc.contributor.authorAsiimwe, Joab
dc.contributor.authorBarigye, Deus Dedet
dc.date.accessioned2024-12-29T12:06:45Z
dc.date.available2024-12-29T12:06:45Z
dc.date.issued2024
dc.description.abstractRingworm, a common fungal infection, affects millions worldwide. Early detection is crucial for effective treatment and prevention of transmission. This report presents a novel ringworm detection system utilizing deep learning and image processing. We carried out our research within a period of seven months and our system performed the desired work. We developed a ringworm detection and diagnosis system that offers rapid and accurate results for early intervention and treatment. Hardware and software. The system employs picture imaging using processor Intel i5, 8GB RAM, and the operating system Windows 10 Pro. The proposed system achieves an accurate detection rate of ringworm. Sampling method was used like Interviewing and Observation.
dc.identifier.citationAsiimwe, J., & Barigye, D. D. (2024). Ringworm Detection and Diagnosis System. Kabale: Kabale University.
dc.identifier.urihttp://hdl.handle.net/20.500.12493/2582
dc.language.isoen
dc.publisherKabale University
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectRingworm Detection
dc.subjectDiagnosis System
dc.titleRingworm Detection and Diagnosis System.
dc.typeThesis

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