Comparison of Shallow and Deep Learning Methods on Classifying the Regional Pattern of Diffuse Lung Disease
Journal Article
·
· Journal of Digital Imaging (Online)
- Asan Medical Center, Biomedical Engineering Research Center, Asan Institute of Life Science (Korea, Republic of)
- VUNO (Korea, Republic of)
- Asan Medical Center, Department of Convergence Medicine, University of Ulsan College of Medicine (Korea, Republic of)
- National Jewish Medical and Research Center, Department of Radiology (United States)
This study aimed to compare shallow and deep learning of classifying the patterns of interstitial lung diseases (ILDs). Using high-resolution computed tomography images, two experienced radiologists marked 1200 regions of interest (ROIs), in which 600 ROIs were each acquired using a GE or Siemens scanner and each group of 600 ROIs consisted of 100 ROIs for subregions that included normal and five regional pulmonary disease patterns (ground-glass opacity, consolidation, reticular opacity, emphysema, and honeycombing). We employed the convolution neural network (CNN) with six learnable layers that consisted of four convolution layers and two fully connected layers. The classification results were compared with the results classified by a shallow learning of a support vector machine (SVM). The CNN classifier showed significantly better performance for accuracy compared with that of the SVM classifier by 6–9%. As the convolution layer increases, the classification accuracy of the CNN showed better performance from 81.27 to 95.12%. Especially in the cases showing pathological ambiguity such as between normal and emphysema cases or between honeycombing and reticular opacity cases, the increment of the convolution layer greatly drops the misclassification rate between each case. Conclusively, the CNN classifier showed significantly greater accuracy than the SVM classifier, and the results implied structural characteristics that are inherent to the specific ILD patterns.
- OSTI ID:
- 22795616
- Journal Information:
- Journal of Digital Imaging (Online), Journal Name: Journal of Digital Imaging (Online) Journal Issue: 4 Vol. 31; ISSN 1618-727X
- Country of Publication:
- United States
- Language:
- English
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