# Multi-Class Classification of Lung Diseases Using CNN Models
## 9 Conclusions  
In this study, we proposed a multi-class classification method of lung disease using CNN model. After making the image in a 1:1 ratio and center cropping it by 87.5%, a classification model of the Multi GAP format was created based on the Noisy Student ImageNet Pretrained weight of the EfficientNet B7 model. In the NIH dataset for benchmarks measurement when lung disease was predicted with the model, the models to which the proposed method was applied showed high performance at 84.86%, 85.15%, and 85.32% of the accuracy criteria, and the model to which all the suggestions were applied showed the most satisfactory performance. Shown in the dataset of Soonchunhyang University Hospital in Cheonan, which includes tuberculosis, an average of 96% accuracy, an average sensitivity of 92.20, and an average specificity of 97.40% were obtained. The average test inference time was 0.2 s, and as a result of not much difference between the validation performance and the test performance, it was confirmed that the generalization of the model was not bad.  
Chest X-ray is the basis of all chest radiographic tests, and it is easy to check the overall outline of the chest at a glance, and it is suitable for follow-up examination because it is easy to observe the chest disease changes. However, there is also a disadvantage that  
Figure 16: Correct predictions and incorrect predictions by classes.  
accurate reading of chest X-ray is difficult due to the complex anatomical structure of the chest. Clinical decision supporting system (CDSS) is a system that helps clinicians make clinical decisions, and is a set of programs that help with more precise diagnosis or prevent misdiagnosis. Therefore, our multi-class classification method of lung disease using CNN model showed high performance (_Accuracy_, 96.22: _Sensitivity_, 92.45: _Specificity_, 97.48) and could be considered applicable to CDSS system for lung diseases.  
Future research intends to proceed with learning methods for more diverse disease types and methods for improving learning performance.  
Conceptualization and supervision, S.C. and M.H.; data curation, methodology, and writing--original draft preparation, H.-C.L., B.R. and H.-u.J.; formal analysis, and writing--review and editing, S.C., M.H. and B.R.; methodology and writing--review and editing, J.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (Project Number: HW20C2174), the framework of an international cooperation program managed by the National Research Foundation of Korea (NRF-2019K1A3A1A20093097), and the Soonchunhyang University Research Fund.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Soonchunhyang University Hospital (2020-12-036-002).  
Patient consent was waived due to the retrospective design of this study.  
No new data were created in this study. Data sharing is not applicable.  
The authors declare no conflict of interest.