Yahya Tashtoush Rasha Obeidat Abdallah Al-Shorman Omar Darwish Mohammad A. Al-Ramahi Dirar Darweesh
Jordan University of Science and Technology Eastern Michigan University Texas A&M University-San Antonio
02-01-2023
Lung cancer is a common type of cancer that causes death if not detectedearly enough. Doctors use computed tomography (CT) images to diagnoselung cancer. The accuracy of the diagnosis relies highly on..
Lung cancer is a common type of cancer that causes death if not detectedearly enough. Doctors use computed tomography (CT) images to diagnoselung cancer. The accuracy of the diagnosis relies highly on the doctor'sexpertise. Recently, clinical decision support systems based on deep learningvaluable recommendations to doctors in their diagnoses. In this paper, wepresent several deep learning models to detect non-small cell lung cancer inCT images and differentiate its main subtypes namely adenocarcinoma,large cell carcinoma, and squamous cell carcinoma. We adopted standardconvolutional neural networks (CNN), visual geometry group-16 (VGG16),and VGG19. Besides, we introduce a variant of the CNN that is augmentedwith convolutional block attention modules (CBAM). CBAM aims to extractinformative features by combining cross-channel and spatial information.We also propose variants of VGG16 and VGG19 that utilize a supportvector machine (SVM) at the classification layer instead of SoftMax. Wevalidated all models in this study through extensive experiments on a CTlung cancer dataset. Experimental results show that supplementing CNNwith CBAM leads to consistent improvements over vanilla CNN. Resultsalso show that the VGG variants that use the SVM classifier outperform theoriginal VGGs by a significant margin.
Originally published as: Tashtoush, Y., Obeidat, R., Al-Shorman, A., Darwish, O., Al-Ramahi, M., & Darweesh, D. (2023). Enhanced convolutional neural network for non-small cell lung cancer classifica..
Originally published as: Tashtoush, Y., Obeidat, R., Al-Shorman, A., Darwish, O., Al-Ramahi, M., & Darweesh, D. (2023). Enhanced convolutional neural network for non-small cell lung cancer classification. International Journal of Electrical & Computer Engineering (2088-8708), 13(1). http://doi.org/10.11591/ijece.v13i1.pp1024-1038
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