Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. The...

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Autores principales:	Nabeela Kausar, Abdul Hameed, Mohsin Sattar, Ramiza Ashraf, Ali Shariq Imran, Muhammad Zain ul Abidin, Ammara Ali
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	skin cancer deep learning ensemble classifier multiclass skin cancer classification model ensemble models Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/5c6ff192889542f4ae1de6c876d3f22f
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spelling	oai:doaj.org-article:5c6ff192889542f4ae1de6c876d3f22f2021-11-25T16:32:29ZMulticlass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models10.3390/app1122105932076-3417https://doaj.org/article/5c6ff192889542f4ae1de6c876d3f22f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10593https://doaj.org/toc/2076-3417Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. Therefore, the classification of skin cancer using machine learning can be beneficial in the diagnosis and treatment of the patients. Several researchers developed skin cancer classification models for binary class but could not extend the research to multiclass classification with better performance ratios. We have developed deep learning-based ensemble classification models for multiclass skin cancer classification. Experimental results proved that the individual deep learners perform better for skin cancer classification, but still the development of ensemble is a meaningful approach since it enhances the classification accuracy. Results show that the accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResNetV2, and VGG-19 are 72%, 91%, 91.4%, 91.7% and 91.8%, respectively. The accuracy of proposed majority voting and weighted majority voting ensemble models are 98% and 98.6%, respectively. The accuracy of proposed ensemble models is higher than the individual deep learners and the dermatologists’ diagnosis accuracy. The proposed ensemble models are compared with the recently developed skin cancer classification approaches. The results show that the proposed ensemble models outperform recently developed multiclass skin cancer classification models.Nabeela KausarAbdul HameedMohsin SattarRamiza AshrafAli Shariq ImranMuhammad Zain ul AbidinAmmara AliMDPI AGarticleskin cancerdeep learningensemble classifiermulticlass skin cancerclassification modelensemble modelsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10593, p 10593 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	skin cancer deep learning ensemble classifier multiclass skin cancer classification model ensemble models Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	skin cancer deep learning ensemble classifier multiclass skin cancer classification model ensemble models Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Nabeela Kausar Abdul Hameed Mohsin Sattar Ramiza Ashraf Ali Shariq Imran Muhammad Zain ul Abidin Ammara Ali Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
description	Skin cancer is a widespread disease associated with eight diagnostic classes. The diagnosis of multiple types of skin cancer is a challenging task for dermatologists due to the similarity of skin cancer classes in phenotype. The average accuracy of multiclass skin cancer diagnosis is 62% to 80%. Therefore, the classification of skin cancer using machine learning can be beneficial in the diagnosis and treatment of the patients. Several researchers developed skin cancer classification models for binary class but could not extend the research to multiclass classification with better performance ratios. We have developed deep learning-based ensemble classification models for multiclass skin cancer classification. Experimental results proved that the individual deep learners perform better for skin cancer classification, but still the development of ensemble is a meaningful approach since it enhances the classification accuracy. Results show that the accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResNetV2, and VGG-19 are 72%, 91%, 91.4%, 91.7% and 91.8%, respectively. The accuracy of proposed majority voting and weighted majority voting ensemble models are 98% and 98.6%, respectively. The accuracy of proposed ensemble models is higher than the individual deep learners and the dermatologists’ diagnosis accuracy. The proposed ensemble models are compared with the recently developed skin cancer classification approaches. The results show that the proposed ensemble models outperform recently developed multiclass skin cancer classification models.
format	article
author	Nabeela Kausar Abdul Hameed Mohsin Sattar Ramiza Ashraf Ali Shariq Imran Muhammad Zain ul Abidin Ammara Ali
author_facet	Nabeela Kausar Abdul Hameed Mohsin Sattar Ramiza Ashraf Ali Shariq Imran Muhammad Zain ul Abidin Ammara Ali
author_sort	Nabeela Kausar
title	Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
title_short	Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
title_full	Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
title_fullStr	Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
title_full_unstemmed	Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
title_sort	multiclass skin cancer classification using ensemble of fine-tuned deep learning models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5c6ff192889542f4ae1de6c876d3f22f
work_keys_str_mv	AT nabeelakausar multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels AT abdulhameed multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels AT mohsinsattar multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels AT ramizaashraf multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels AT alishariqimran multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels AT muhammadzainulabidin multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels AT ammaraali multiclassskincancerclassificationusingensembleoffinetuneddeeplearningmodels
_version_	1718413136931848192

Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

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