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Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities.

Authors :
Esha, Jannatul Ferdous
Islam, Tahmidul
Pranto, Md. Appel Mahmud
Borno, Abrar Siam
Faruqui, Nuruzzaman
Yousuf, Mohammad Abu
Azad, AKM
Al-Moisheer, Asmaa Soliman
Alotaibi, Naif
Alyami, Salem A.
Moni, Mohammad Ali
Source :
Diagnostics (2075-4418); Oct2024, Vol. 14 Issue 20, p2282, 21p
Publication Year :
2024

Abstract

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model's performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
14
Issue :
20
Database :
Complementary Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
180557628
Full Text :
https://doi.org/10.3390/diagnostics14202282