Abstract
This paper presents the development of a non-invasive image-analysis method for improving the diagnostic accuracy of mediastinal lymph node metastasis in patients with lung cancer. The approach adopts pretrained deep learning models incorporated with the geostatistical simulation of texture of benign and metastatic lung lymph nodes in computed tomography (CT) images for classification. Using 271 CT samples of mediastinal lymph nodes collected from 148 patients with lung cancer, deep-learning models coupled with the stochastic simulated data augmentation provide the best classification results. The simulation of texture in these medical images was able to discover rich radiomic features to ascertain subtle difference between benign and metastatic lymph nodes and enhance the performance of the deep-learning models for complex pattern classification. The proposed approach is very promising to be utilized as a computerized tool for medical image analysis.