@article{10.15701/kcgs.2022.28.1.1,
author = {Ma, Serie AND Ahn, Gahee AND Hong, Helen},
journal = {Journal of the Korea Computer Graphics Society},
publisher = {The Korea Computer Graphics Society},
title = {Chest CT Image Patch-Based CNN Classification and Visualization for Predicting Recurrence of Non-Small Cell Lung Cancer Patients},
year = {2022},
volume = {28},
number = {1},
url = {https://doi.org/10.15701/kcgs.2022.28.1.1},
pages = {1-9},
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%. },
doi = {10.15701/kcgs.2022.28.1.1}
}