Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification
Melanoma is a type of a skin cancer or lesion which has the detrimental ramifications on the human health but with early diagnosis it can be cured easily. The actual identification of skin lesion is very challenging because of factors like a very minute difference between lesion and skin and it is v...
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John Wiley & Sons, Inc.
2022
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| Online Access: | http://ir.unimas.my/id/eprint/47355/1/Published_willy.pdf http://ir.unimas.my/id/eprint/47355/ https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6907 https://doi.org/10.1002/cpe.6907 |
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my.unimas.ir-473552025-01-16T06:52:29Z http://ir.unimas.my/id/eprint/47355/ Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification Adil H., Khan Dayang Nurfatimah, Awang Iskandar Jawad F., Al-Asad Hiren, Mewada Muhammad Abid, Sherazi QA75 Electronic computers. Computer science Melanoma is a type of a skin cancer or lesion which has the detrimental ramifications on the human health but with early diagnosis it can be cured easily. The actual identification of skin lesion is very challenging because of factors like a very minute difference between lesion and skin and it is very difficult to differentiate among skin cancer types due to visual comparability. Hence an autonomous system for the diagnosis of true skin cancer type is very useful. In this article, we took the leverage of ensemble learning by combining the features of deep learning architectures with traditional features extraction approaches. For segmentation, we have two pipelines for the feature extraction. We extract the features through traditional split and merge approach as well as from deep learning algorithms of contextual encoding along with the attention mechanism. Later we combine the features of both architectures and predict the segmented region through intersection over union mechanism. After that segmented region is classified into three types of skin lesion using hybrid features of Alex-Net and VGG-16 through the transfer learning approach. The evaluation has been performed using the ISIC and PH2 datasets for which achieved segmentation accuracy is 97.8% and 96.7%, respectively.Moreover, hybrid classification network able to attain the 98.2% accuracy. John Wiley & Sons, Inc. 2022 Article PeerReviewed text en http://ir.unimas.my/id/eprint/47355/1/Published_willy.pdf Adil H., Khan and Dayang Nurfatimah, Awang Iskandar and Jawad F., Al-Asad and Hiren, Mewada and Muhammad Abid, Sherazi (2022) Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification. Concurrency Computation Practice and Experience, 34 (13). pp. 1-19. ISSN 1532-0634 https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6907 https://doi.org/10.1002/cpe.6907 |
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QA75 Electronic computers. Computer science Adil H., Khan Dayang Nurfatimah, Awang Iskandar Jawad F., Al-Asad Hiren, Mewada Muhammad Abid, Sherazi Ensemble learning of deep learning and traditional machine learning approaches for skin lesion segmentation and classification |
| description |
Melanoma is a type of a skin cancer or lesion which has the detrimental ramifications on the human health but with early diagnosis it can be cured easily. The actual identification of skin lesion is very challenging because of factors like a very minute difference between lesion and skin and it is very difficult to differentiate among skin cancer types
due to visual comparability. Hence an autonomous system for the diagnosis of true skin cancer type is very useful. In this article, we took the leverage of ensemble learning by combining the features of deep learning architectures with traditional features extraction approaches. For segmentation, we have two pipelines for the feature extraction. We extract the features through traditional split and merge approach as well as from deep learning algorithms of contextual encoding along with the attention mechanism. Later we combine the features of both architectures and predict the segmented region through intersection over union mechanism. After that segmented region is classified into three types of skin lesion using hybrid features of Alex-Net and VGG-16 through the transfer learning approach. The evaluation has been performed using the ISIC and PH2 datasets for which achieved segmentation accuracy is 97.8% and 96.7%, respectively.Moreover, hybrid classification network able to attain the 98.2% accuracy. |
| format |
Article |
| author |
Adil H., Khan Dayang Nurfatimah, Awang Iskandar Jawad F., Al-Asad Hiren, Mewada Muhammad Abid, Sherazi |
| author_facet |
Adil H., Khan Dayang Nurfatimah, Awang Iskandar Jawad F., Al-Asad Hiren, Mewada Muhammad Abid, Sherazi |
| author_sort |
Adil H., Khan |
| title |
Ensemble learning of deep learning and traditional machine
learning approaches for skin lesion segmentation
and classification |
| title_short |
Ensemble learning of deep learning and traditional machine
learning approaches for skin lesion segmentation
and classification |
| title_full |
Ensemble learning of deep learning and traditional machine
learning approaches for skin lesion segmentation
and classification |
| title_fullStr |
Ensemble learning of deep learning and traditional machine
learning approaches for skin lesion segmentation
and classification |
| title_full_unstemmed |
Ensemble learning of deep learning and traditional machine
learning approaches for skin lesion segmentation
and classification |
| title_sort |
ensemble learning of deep learning and traditional machine
learning approaches for skin lesion segmentation
and classification |
| publisher |
John Wiley & Sons, Inc. |
| publishDate |
2022 |
| url |
http://ir.unimas.my/id/eprint/47355/1/Published_willy.pdf http://ir.unimas.my/id/eprint/47355/ https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6907 https://doi.org/10.1002/cpe.6907 |
| _version_ |
1822896181365178368 |
| score |
13.235362 |