Computed tomography, Convolutional block attention module, Convolutional neural networks, Deep learning, Lung cancer, Non-small cell carcinoma, VGG16
Lung cancer is a common type of cancer that causes death if not detected
early enough. Doctors use computed tomography (CT) images to diagnose
lung cancer. The accuracy of the diagnosis relies highly on the doctor's
expertise. Recently, clinical decision support systems based on deep learning
valuable recommendations to doctors in their diagnoses. In this paper, we
present several deep learning models to detect non-small cell lung cancer in
CT images and differentiate its main subtypes namely adenocarcinoma,
large cell carcinoma, and squamous cell carcinoma. We adopted standard
convolutional neural networks (CNN), visual geometry group-16 (VGG16),
and VGG19. Besides, we introduce a variant of the CNN that is augmented
with convolutional block attention modules (CBAM). CBAM aims to extract
informative features by combining cross-channel and spatial information.
We also propose variants of VGG16 and VGG19 that utilize a support
vector machine (SVM) at the classification layer instead of SoftMax. We
validated all models in this study through extensive experiments on a CT
lung cancer dataset. Experimental results show that supplementing CNN
with CBAM leads to consistent improvements over vanilla CNN. Results
also show that the VGG variants that use the SVM classifier outperform the
original VGGs by a significant margin.
Digital Object Identifier (DOI)
Tashtoush, Yahya; Obeidat, Rasha; Al-Shorman, Abdallah; Darwish, Omar; Al-Ramahi, Mohammad A.; and Darweesh, Dirar, "Enhanced Convolutional Neural Network for Non-Small Cell Lung Cancer Classification" (2023). Computer Information Systems Faculty Publications. 16.
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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