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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| Format: | Article |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10593 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:44:07Z |
| format | Article |
| id | doaj.art-5c6ff192889542f4ae1de6c876d3f22f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:07Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5c6ff192889542f4ae1de6c876d3f22f2023-11-22T22:16:02ZengMDPI AGApplied Sciences2076-34172021-11-0111221059310.3390/app112210593Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning ModelsNabeela Kausar0Abdul Hameed1Mohsin Sattar2Ramiza Ashraf3Ali Shariq Imran4Muhammad Zain ul Abidin5Ammara Ali6Department of Computing and Technology, Iqra University, Islamabad 44000, PakistanDepartment of Computing and Technology, Iqra University, Islamabad 44000, PakistanMIS Division, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Islamabad 44000, PakistanSchool of Informatics and Applied Mathematics, Universiti Malaysia Terengganu (UMT), Terengganu 21030, MalaysiaThe Norwegian Colour and Visual Computing Laboratory (ColorLab), Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, NorwayDepartment of Computing and Technology, Iqra University, Islamabad 44000, PakistanDepartment of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, NorwaySkin 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.https://www.mdpi.com/2076-3417/11/22/10593skin cancerdeep learningensemble classifiermulticlass skin cancerclassification modelensemble models |
| spellingShingle | 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 Applied Sciences skin cancer deep learning ensemble classifier multiclass skin cancer classification model ensemble models |
| title | 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_short | 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 |
| topic | skin cancer deep learning ensemble classifier multiclass skin cancer classification model ensemble models |
| url | https://www.mdpi.com/2076-3417/11/22/10593 |
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