Implementation of CNN Voting based Technique for Classification of Lung Images
Tanga, Champa (Rajiv Gandhi University, Doimukh, Arunachal Pradesh, India)
Roy, Amarjit (Ghani Khan Choudhury Institute of Engineering and Technology, Malda, West Bengal, India)
Rahul, Jagdeep (Rajiv Gandhi University, Doimukh, Arunachal Pradesh, India)
Islam, Mohiul (Vellore Institute of Technology, Vellore, Tamil Nadu, India)
Sain, Chiranjit (Ghani Khan Choudhury Institute of Engineering and Technology, Malda, West Bengal, India)
Ustun, Taha Selim (Fukushima Renewable Energy Institute, Koriyama, Japan)

Data:	2026
Resum:	Lung-related disorders such as pneumonia, cancer, and tuberculosis remain significant health concerns in recent decades. This research proposes a voting-based ensemble of pretrained CNN models to accurately classify lung disorders, such as pneumonia, tuberculosis, and COVID-19, from medical images. The suggested method improves diagnostic performance by aggregating predictions using majority voting, resulting in enhanced accuracy, sensitivity, and robustness compared to traditional techniques. This study presents a CNN-based multi-tier classification framework for the diagnosis of lung disorders utilizing transfer learning with ResNet50, AlexNet, and VGG19. A voting-based fusion method integrates results from separate models to improve diagnostic precision. The suggested CNN voting-based classifier was evaluated on a dataset of more than 900 lung pictures, encompassing pneumonia, tuberculosis, COVID-19, and normal cases. Three pretrained models-ResNet50, AlexNet, and VGG19-were employed utilizing a voting-based ensemble approach to augment classification robustness. The experimental findings indicated that the fusion model surpassed individual CNNs, with an accuracy of 92. 30% and a sensitivity of 100%. Performance was assessed utilizing measures including accuracy, precision, specificity, sensitivity, and AUC, and compared to state-of-the-art approaches to illustrate its efficacy.
Drets:	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.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Convolutional Neural Network ; Ensemble Learning ; Medical Image Analysis ; Voting based decision fusion ; Lung disease classification
Publicat a:	ELCVIA, Vol. 25, Num. 2 (2026) , p. 38-54 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/2366
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000008486
DOI: 10.5565/rev/elcvia.2366

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