Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer
Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin-related consolidated malignancies. The number of cases is gro...
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8756036/ |
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| author | Muhammad Qasim Khan Ayyaz Hussain Saeed Ur Rehman Umair Khan Muazzam Maqsood Kashif Mehmood Muazzam A. Khan |
| author_facet | Muhammad Qasim Khan Ayyaz Hussain Saeed Ur Rehman Umair Khan Muazzam Maqsood Kashif Mehmood Muazzam A. Khan |
| author_sort | Muhammad Qasim Khan |
| collection | DOAJ |
| description | Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin-related consolidated malignancies. The number of cases is growing among young people, but if it is diagnosed at an earlier stage, then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for melanoma are very high. In this paper, we propose an intelligent system to detect and distinguish melanoma from nevus by using the state-of-the-art image processing techniques. At first, the Gaussian filter is used for removing noise from the skin lesion of the acquired images followed by the use of improved K-mean clustering to segment out the lesion. A distinctive hybrid superfeature vector is formed by the extraction of textural and color features from the lesion. Support vector machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features, and compare the classification results with the other techniques present in the literature. The proposed methodology is tested on the DERMIS dataset having a total number of 397 skin cancer images: 146 are melanoma and 251 are nevus skin lesions. Our proposed methodology archives encouraging results having 96% accuracy. |
| format | Article |
| id | doaj-art-9b8e16b4b93e408ca0634b5e4127f617 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9b8e16b4b93e408ca0634b5e4127f6172025-01-30T00:00:47ZengIEEEIEEE Access2169-35362019-01-017901329014410.1109/ACCESS.2019.29268378756036Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin CancerMuhammad Qasim Khan0https://orcid.org/0000-0002-3569-5012Ayyaz Hussain1Saeed Ur Rehman2https://orcid.org/0000-0001-9167-1122Umair Khan3Muazzam Maqsood4https://orcid.org/0000-0002-2709-0849Kashif Mehmood5Muazzam A. Khan6Department of Computer Science and Software Engineering, International Islamic University, Islamabad, PakistanDepartment of Computer Science and Software Engineering, International Islamic University, Islamabad, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, PakistanDepartment of Computing, SEECS, National University of Sciences and Technology, Islamabad, PakistanMelanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin-related consolidated malignancies. The number of cases is growing among young people, but if it is diagnosed at an earlier stage, then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for melanoma are very high. In this paper, we propose an intelligent system to detect and distinguish melanoma from nevus by using the state-of-the-art image processing techniques. At first, the Gaussian filter is used for removing noise from the skin lesion of the acquired images followed by the use of improved K-mean clustering to segment out the lesion. A distinctive hybrid superfeature vector is formed by the extraction of textural and color features from the lesion. Support vector machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features, and compare the classification results with the other techniques present in the literature. The proposed methodology is tested on the DERMIS dataset having a total number of 397 skin cancer images: 146 are melanoma and 251 are nevus skin lesions. Our proposed methodology archives encouraging results having 96% accuracy.https://ieeexplore.ieee.org/document/8756036/MelanomanevusfeatureK-means clusteringcentroid selection |
| spellingShingle | Muhammad Qasim Khan Ayyaz Hussain Saeed Ur Rehman Umair Khan Muazzam Maqsood Kashif Mehmood Muazzam A. Khan Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer IEEE Access Melanoma nevus feature K-means clustering centroid selection |
| title | Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer |
| title_full | Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer |
| title_fullStr | Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer |
| title_full_unstemmed | Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer |
| title_short | Classification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer |
| title_sort | classification of melanoma and nevus in digital images for diagnosis of skin cancer |
| topic | Melanoma nevus feature K-means clustering centroid selection |
| url | https://ieeexplore.ieee.org/document/8756036/ |
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