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http://dx.doi.org/10.9717/kmms.2020.24.3.373

Ensemble Learning Based on Tumor Internal and External Imaging Patch to Predict the Recurrence of Non-small Cell Lung Cancer Patients in Chest CT Image  

Lee, Ye-Sel (Dept. of Software Convergence, Seoul Women's University)
Cho, A-Hyun (Major of Bio & Environmental Technology, Seoul Women's University)
Hong, Helen (Dept. of Software Convergence, Seoul Women's University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.3, 2021 , pp. 373-381 More about this Journal
Abstract
In this paper, we propose a classification model based on convolutional neural network(CNN) for predicting 2-year recurrence in non-small cell lung cancer(NSCLC) patients using preoperative chest CT images. Based on the region of interest(ROI) defined as the tumor internal and external area, the input images consist of an intratumoral patch, a peritumoral patch and a peritumoral texture patch focusing on the texture information of the peritumoral patch. Each patch is trained through AlexNet pretrained on ImageNet to explore the usefulness and performance of various patches. Additionally, ensemble learning of network trained with each patch analyzes the performance of different patch combination. Compared with all results, the ensemble model with intratumoral and peritumoral patches achieved the best performance (ACC=98.28%, Sensitivity=100%, NPV=100%).
Keywords
Recurrence Prediction; Classification; Deep Learning; Ensemble; Non-Small Cell Lung Cancer(NSCLC);
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