State-of-the-art DNN techniques for lung cancer diagnosis using chest CT scans : a review
Sakshiwala (National Institute of Technology Patna (Índia))
Singh, Maheshwari Prasad (National Institute of Technology Patna (Índia))

Date:	2025
Abstract:	This paper reviews state-of-the-art literature on the early diagnosis of lung cancer with deep neural network techniques and chest CT scans. First, a brief introduction to the significance of lung cancer and the need for this review is stated. The architectures of the deep neural networks, evaluation methods, and the comprehensive review of recent progress in lung cancer diagnosis based on deep neural network techniques are provided. Further, the comparative analysis of the literature is presented. A critical discussion on the existing datasets, various methodologies, and challenges in the diagnosis are presented. The performances of deep neural network-based techniques for segmentation, nodule detection, and nodule classification are also discussed. This review covers the malignancy classification along with the nodule detection tasks. Thus, this may provide necessary information to all the researchers to prepare a robust methodology for early detection of lung cancer and hence proper diagnosis.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Convolution neural network ; Pulmonary nodule ; Nodule detection ; Nodule classification ; 2D ; 3D
Published in:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 24 Núm. 2 (2025) , p. 1-27 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1597
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000001042
DOI: 10.5565/rev/elcvia.1597

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