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Skin cancer diagnostics: a VGGEnsemble approach

The human skin is the largest organ of the human body, and it is highly susceptible to lesions. This study attempts to classify two distinct classes of malignant skin cancers, i.e., Actinic Keratosis (AK) and Basal Cell Carcinoma (BCC), as well as Dermatofibroma (DF), which is benign. A total of 330...

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Main Authors: Arzmi, Mohd Hafiz, P.P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, Mohd Razman, Mohd Azraai, Gan, Hong-Seng, Mohd Khairuddin, Ismail, Ab. Nasir, Ahmad Fakhri
Format: Book Chapter
Language:English
Published: Springer 2023
Subjects:
R Medicine (General)
Online Access:http://irep.iium.edu.my/103899/1/103899_Skin%20cancer%20diagnostics%20a%20VGGEnsemble%20approach.pdf
http://irep.iium.edu.my/103899/
https://link.springer.com/chapter/10.1007/978-981-19-8937-7_5
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spelling my.iium.irep.1038992023-03-09T06:26:47Z http://irep.iium.edu.my/103899/ Skin cancer diagnostics: a VGGEnsemble approach Arzmi, Mohd Hafiz P.P. Abdul Majeed, Anwar Musa, Rabiu Muazu Mohd Razman, Mohd Azraai Gan, Hong-Seng Mohd Khairuddin, Ismail Ab. Nasir, Ahmad Fakhri R Medicine (General) The human skin is the largest organ of the human body, and it is highly susceptible to lesions. This study attempts to classify two distinct classes of malignant skin cancers, i.e., Actinic Keratosis (AK) and Basal Cell Carcinoma (BCC), as well as Dermatofibroma (DF), which is benign. A total of 330 dermoscopy images were split into the 70:15:15 ratio for training, testing and validation, respectively. Different VGG-Logistic Regression (LR) pipelines, i.e., VGG16-LR and VGG19-LR, were formulated. In addition, the effect of combining the features extracted from both VGG models, dubbed as VGGEnsemble, was also investigated. It was demonstrated from the study that the ensemble model yielded a better classification accuracy than its standalone versions. Therefore, it could be concluded that the performance of the pipeline is improved through this approach and subsequently could aid the diagnostics of different types of skin diseases by dermatologists. Springer 2023-01-19 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/103899/1/103899_Skin%20cancer%20diagnostics%20a%20VGGEnsemble%20approach.pdf Arzmi, Mohd Hafiz and P.P. Abdul Majeed, Anwar and Musa, Rabiu Muazu and Mohd Razman, Mohd Azraai and Gan, Hong-Seng and Mohd Khairuddin, Ismail and Ab. Nasir, Ahmad Fakhri (2023) Skin cancer diagnostics: a VGGEnsemble approach. In: Deep Learning in Cancer Diagnostics. SpringerBriefs in Applied Sciences and Technology (1). Springer, Singapore, pp. 27-32. ISBN 978-981-19-8936-0 https://link.springer.com/chapter/10.1007/978-981-19-8937-7_5 10.1007/978-981-19-8937-7_5
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic R Medicine (General)
spellingShingle R Medicine (General)
Arzmi, Mohd Hafiz
P.P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
Mohd Razman, Mohd Azraai
Gan, Hong-Seng
Mohd Khairuddin, Ismail
Ab. Nasir, Ahmad Fakhri
Skin cancer diagnostics: a VGGEnsemble approach
description The human skin is the largest organ of the human body, and it is highly susceptible to lesions. This study attempts to classify two distinct classes of malignant skin cancers, i.e., Actinic Keratosis (AK) and Basal Cell Carcinoma (BCC), as well as Dermatofibroma (DF), which is benign. A total of 330 dermoscopy images were split into the 70:15:15 ratio for training, testing and validation, respectively. Different VGG-Logistic Regression (LR) pipelines, i.e., VGG16-LR and VGG19-LR, were formulated. In addition, the effect of combining the features extracted from both VGG models, dubbed as VGGEnsemble, was also investigated. It was demonstrated from the study that the ensemble model yielded a better classification accuracy than its standalone versions. Therefore, it could be concluded that the performance of the pipeline is improved through this approach and subsequently could aid the diagnostics of different types of skin diseases by dermatologists.
format Book Chapter
author Arzmi, Mohd Hafiz
P.P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
Mohd Razman, Mohd Azraai
Gan, Hong-Seng
Mohd Khairuddin, Ismail
Ab. Nasir, Ahmad Fakhri
author_facet Arzmi, Mohd Hafiz
P.P. Abdul Majeed, Anwar
Musa, Rabiu Muazu
Mohd Razman, Mohd Azraai
Gan, Hong-Seng
Mohd Khairuddin, Ismail
Ab. Nasir, Ahmad Fakhri
author_sort Arzmi, Mohd Hafiz
title Skin cancer diagnostics: a VGGEnsemble approach
title_short Skin cancer diagnostics: a VGGEnsemble approach
title_full Skin cancer diagnostics: a VGGEnsemble approach
title_fullStr Skin cancer diagnostics: a VGGEnsemble approach
title_full_unstemmed Skin cancer diagnostics: a VGGEnsemble approach
title_sort skin cancer diagnostics: a vggensemble approach
publisher Springer
publishDate 2023
url http://irep.iium.edu.my/103899/1/103899_Skin%20cancer%20diagnostics%20a%20VGGEnsemble%20approach.pdf
http://irep.iium.edu.my/103899/
https://link.springer.com/chapter/10.1007/978-981-19-8937-7_5
_version_ 1761616130753953792
score 13.250246

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