Automatic multiclass classification of laryngeal cancer using deep convolution neural networks

Autor:	Ramesh Munirathinam, M. Tamilnidhi, Rajasekaran Thangaraj, Sivaraman Eswaran, Gokul Chandrasekaran, Neelam Sanjeev Kumar
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	artificial intelligence convolutional neural nets health care Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Electronics Letters, Vol 60, Iss 1, Pp n/a-n/a (2024)
Druh dokumentu:	article
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.13070
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/a3a4acd79fc44df4a2250ec237a5f6b8 Zobrazit plný text záznamu Plný text View record in DOAJ