Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar

Chronic skin disease such as psoriasis and eczema are two of the most common skin disease affecting the human body especially in Malaysia. These skin diseases can cause serious health and financial effects if not recognised and treated early. Early detection of disease severity, as well as advice on...

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Main Author: Omar, Muhammad Amsyar
Format: Thesis
Language:English
Published: 2022
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/59384/1/59384.pdf
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spelling my-uitm-ir.593842022-07-21T15:38:50Z Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar 2022-01 Omar, Muhammad Amsyar Electronic Computers. Computer Science Mobile computing Neural networks (Computer science) Chronic skin disease such as psoriasis and eczema are two of the most common skin disease affecting the human body especially in Malaysia. These skin diseases can cause serious health and financial effects if not recognised and treated early. Early detection of disease severity, as well as advice on skincare and medicine, can help keep the condition from worsening. However, the current diagnosis might be time-consuming and expensive. Hence, this project aimed to develop automated skin disease detection focusing on psoriasis and eczema as the common skin disease in Malaysia. To accomplish this, skin disease images were pre-processed to filter and segment the image by enhancing, removing noise, and segmenting the image. Then, the method Gray Level Co-Occurrence Matrix (GLCM) was used to extract features of the skin disease images that could be obtained correctly. The identification of the skin disease is performed in the enhanced images using Support Vector Machine (SVM) classifier. A set of 20 different skin disease images were analysed and utilized, giving an overall accuracy of 90% for skin disease identification. These findings indicate that the proposed system can assist patients and dermatologist in determining the type of disease from an image of the affected region during the early stages of skin disease. 2022-01 Thesis https://ir.uitm.edu.my/id/eprint/59384/ https://ir.uitm.edu.my/id/eprint/59384/1/59384.pdf text en public degree Universiti Teknologi MARA, Perak Faculty of Computer and Mathematical Sciences Zulkifli, Zalikha
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
language English
advisor Zulkifli, Zalikha
topic Electronic Computers
Computer Science
Mobile computing
Neural networks (Computer science)
spellingShingle Electronic Computers
Computer Science
Mobile computing
Neural networks (Computer science)
Omar, Muhammad Amsyar
Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar
description Chronic skin disease such as psoriasis and eczema are two of the most common skin disease affecting the human body especially in Malaysia. These skin diseases can cause serious health and financial effects if not recognised and treated early. Early detection of disease severity, as well as advice on skincare and medicine, can help keep the condition from worsening. However, the current diagnosis might be time-consuming and expensive. Hence, this project aimed to develop automated skin disease detection focusing on psoriasis and eczema as the common skin disease in Malaysia. To accomplish this, skin disease images were pre-processed to filter and segment the image by enhancing, removing noise, and segmenting the image. Then, the method Gray Level Co-Occurrence Matrix (GLCM) was used to extract features of the skin disease images that could be obtained correctly. The identification of the skin disease is performed in the enhanced images using Support Vector Machine (SVM) classifier. A set of 20 different skin disease images were analysed and utilized, giving an overall accuracy of 90% for skin disease identification. These findings indicate that the proposed system can assist patients and dermatologist in determining the type of disease from an image of the affected region during the early stages of skin disease.
format Thesis
qualification_level Bachelor degree
author Omar, Muhammad Amsyar
author_facet Omar, Muhammad Amsyar
author_sort Omar, Muhammad Amsyar
title Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar
title_short Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar
title_full Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar
title_fullStr Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar
title_full_unstemmed Identification of skin disease using Gray Level Co-occurrence Matrix and Support Vector Machine / Muhammad Amsyar Omar
title_sort identification of skin disease using gray level co-occurrence matrix and support vector machine / muhammad amsyar omar
granting_institution Universiti Teknologi MARA, Perak
granting_department Faculty of Computer and Mathematical Sciences
publishDate 2022
url https://ir.uitm.edu.my/id/eprint/59384/1/59384.pdf
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