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Image analysis model for skin disease detection: framework

Haddad, Alaa and Hameed, Shihab Ahmed (2018) Image analysis model for skin disease detection: framework. In: 7th International Conference on Computer and Communication Engineering, ICCCE 2018, 19 - 20 September 2018, International Islamic University Malaysia (IIUM), Gombak , Kuala Lumpur.

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Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model is proposed to provide more objective and reliable solution. Many researches were done to help detect skin diseases like skin cancer and tumor skin. But the accurate recognition of the disease is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between Disease and non-Disease area, etc. This paper aims to detect skin disease from the skin image and to analyze this image by applying filter to remove noise or unwanted things, convert the image to grey to help in the processing and get the useful information. This help to give evidence for any type of skin disease and illustrate emergency orientation. Analysis result of this study can support doctor to help in initial diagnoses and to know the type of disease. That is compatible with skin and to avoid side effects.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 4428/73102
Uncontrolled Keywords: skin disease detection, image classification, image segmentation.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
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 shihab hameed
Date Deposited: 05 Jul 2019 15:10
Last Modified: 05 Jul 2019 15:10
URI: http://irep.iium.edu.my/id/eprint/73102

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