Multi-Class Classification of Lung Diseases Using CNN Models
Source abstract
Abstract
In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging · Traditional Chinese Medicine Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea
Department of Software Convergence, Soonchunhyang University, Asan 31538, Korea
Haewootech Co., Ltd., Busan 46742, Korea
HDT, Co., Ltd., Gwangju 61042, Korea
Department of Otolaryngology-Head and Neck Surgery, Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan 31151, Korea
Funding
The Korea Medical Device Development Fund grant funded by the Korea government · The framework of an international cooperation program managed by the National Research Foundation of Korea · The Soonchunhyang University Research Fund
References (32)
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