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http://dx.doi.org/10.15701/kcgs.2022.28.1.1

Chest CT Image Patch-Based CNN Classification and Visualization for Predicting Recurrence of Non-Small Cell Lung Cancer Patients  

Ma, Serie (Seoul Women's University, Department of Software Convergence)
Ahn, Gahee (Seoul Women's University, Department of Software Convergence)
Hong, Helen (Seoul Women's University, Department of Software Convergence)
Publication Information
Journal of the Korea Computer Graphics Society / v.28, no.1, 2022 , pp. 1-9 More about this Journal
Abstract
Non-small cell lung cancer (NSCLC) accounts for a high proportion of 85% among all lung cancer and has a significantly higher mortality rate (22.7%) compared to other cancers. Therefore, it is very important to predict the prognosis after surgery in patients with non-small cell lung cancer. In this study, the types of preoperative chest CT image patches for non-small cell lung cancer patients with tumor as a region of interest are diversified into five types according to tumor-related information, and performance of single classifier model, ensemble classifier model with soft-voting method, and ensemble classifier model using 3 input channels for combination of three different patches using pre-trained ResNet and EfficientNet CNN networks are analyzed through misclassification cases and Grad-CAM visualization. As a result of the experiment, the ResNet152 single model and the EfficientNet-b7 single model trained on the peritumoral patch showed accuracy of 87.93% and 81.03%, respectively. In addition, ResNet152 ensemble model using the image, peritumoral, and shape-focused intratumoral patches which were placed in each input channels showed stable performance with an accuracy of 87.93%. Also, EfficientNet-b7 ensemble classifier model with soft-voting method using the image and peritumoral patches showed accuracy of 84.48%.
Keywords
Non-Small Cell Lung Cancer(NSCLC); Recurrence Prediction; Deep Learning; Classification; Ensemble Learning; Convolutional Neural Network(CNN);
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Times Cited By KSCI : 1  (Citation Analysis)
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