Automatic multiclass classification of laryngeal cancer using deep convolution neural networks
Abstract In this work, the classification of laryngeal cancer is attempted using deeply learned features obtained using Inception V3, Squeezenet, and VGG‐16 embedders in the Orange toolbox. Machine learning algorithms such as KNN, SVM, random forest, decision tree, and neural network classifiers are...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Electronics Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ell2.13070 |
| _version_ | 1797359362686058496 |
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| author | Ramesh Munirathinam M. Tamilnidhi Rajasekaran Thangaraj Sivaraman Eswaran Gokul Chandrasekaran Neelam Sanjeev Kumar |
| author_facet | Ramesh Munirathinam M. Tamilnidhi Rajasekaran Thangaraj Sivaraman Eswaran Gokul Chandrasekaran Neelam Sanjeev Kumar |
| author_sort | Ramesh Munirathinam |
| collection | DOAJ |
| description | Abstract In this work, the classification of laryngeal cancer is attempted using deeply learned features obtained using Inception V3, Squeezenet, and VGG‐16 embedders in the Orange toolbox. Machine learning algorithms such as KNN, SVM, random forest, decision tree, and neural network classifiers are employed to classify the stages or categories of laryngeal cancer. The ranking of deep learning feature values is carried out using state‐of‐the‐art metrics such as information gain, information gain ratio, chi‐square, and reliefF. It is observed that the performance of the algorithms is affected by the cross‐validation. |
| first_indexed | 2024-03-08T15:16:28Z |
| format | Article |
| id | doaj.art-a3a4acd79fc44df4a2250ec237a5f6b8 |
| institution | Directory Open Access Journal |
| issn | 0013-5194 1350-911X |
| language | English |
| last_indexed | 2024-03-08T15:16:28Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Electronics Letters |
| spelling | doaj.art-a3a4acd79fc44df4a2250ec237a5f6b82024-01-10T11:00:33ZengWileyElectronics Letters0013-51941350-911X2024-01-01601n/an/a10.1049/ell2.13070Automatic multiclass classification of laryngeal cancer using deep convolution neural networksRamesh Munirathinam0M. Tamilnidhi1Rajasekaran Thangaraj2Sivaraman Eswaran3Gokul Chandrasekaran4Neelam Sanjeev Kumar5Department of Biomedical Engineering Karpagam Academy of Higher Education Coimbatore IndiaDepartment of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore IndiaDepartment of Computer Science and Engineering, Centre for IoT and AI(CITI) KPR Institute of Engineering and Technology Coimbatore IndiaDepartment of Electrical and Computer Engineering Curtin University Miri MalaysiaDepartment of Electrical and Electronics Engineering Velalar College of Engineering and Technology Erode IndiaDepartment of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani campus Chennai IndiaAbstract In this work, the classification of laryngeal cancer is attempted using deeply learned features obtained using Inception V3, Squeezenet, and VGG‐16 embedders in the Orange toolbox. Machine learning algorithms such as KNN, SVM, random forest, decision tree, and neural network classifiers are employed to classify the stages or categories of laryngeal cancer. The ranking of deep learning feature values is carried out using state‐of‐the‐art metrics such as information gain, information gain ratio, chi‐square, and reliefF. It is observed that the performance of the algorithms is affected by the cross‐validation.https://doi.org/10.1049/ell2.13070artificial intelligenceconvolutional neural netshealth care |
| spellingShingle | Ramesh Munirathinam M. Tamilnidhi Rajasekaran Thangaraj Sivaraman Eswaran Gokul Chandrasekaran Neelam Sanjeev Kumar Automatic multiclass classification of laryngeal cancer using deep convolution neural networks Electronics Letters artificial intelligence convolutional neural nets health care |
| title | Automatic multiclass classification of laryngeal cancer using deep convolution neural networks |
| title_full | Automatic multiclass classification of laryngeal cancer using deep convolution neural networks |
| title_fullStr | Automatic multiclass classification of laryngeal cancer using deep convolution neural networks |
| title_full_unstemmed | Automatic multiclass classification of laryngeal cancer using deep convolution neural networks |
| title_short | Automatic multiclass classification of laryngeal cancer using deep convolution neural networks |
| title_sort | automatic multiclass classification of laryngeal cancer using deep convolution neural networks |
| topic | artificial intelligence convolutional neural nets health care |
| url | https://doi.org/10.1049/ell2.13070 |
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