Proposal for the Effective Utilization of Voxel Data for the Classification of Diffuse Lung Diseases in Chest CT Images

Abstract

In recent years, improvements in GPU performance have made it possible to construct deep learning models for voxel data. However, chest CT images typically have a resolution of around 200-300×512×512 per case, and processing data of this scale requires substantial GPU memory and extensive training time. Therefore, we propose methods for the effective utilization of voxel data in chest CT images. The first method uses data obtained by sampling slices based on the three axes: the coronal axis, the sagittal axis, and the axial axis. The classification performance for the minority class was evaluated using precision-recall area under the curve (PR-AUC)，and the results showed a mean accuracy of 0.9948 with a standard deviation of 0.0103 for data in the same domain as the training data and a mean of 0.5177 with a standard deviation of 0.1320 for data in different domains. The second method extracts brightness and texture features from the images and inputs the concatenated data into the model. This method maintained high classification performance for data in the same domain as the training data with a mean of 0.9071 and a standard deviation of 0.1008, while also achieving high classification performance with a mean of 0.8174 and a standard deviation of 0.0546 for data in different domains.