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Conference Paper
A study on deep learning-based classification for Pneumonia detection
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- Authors
- Seong Won Jo, Jinwuk Seok
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1-3
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we investigate the various aspects of methodologies in deep learning-based pneumonia classification using chest x-ray images. As widely known, selecting appropriate hyper-parameters is essential for increasing the classification performance in convolution neural networks(CNN). We experiment with various hyper-parameters, including the number of layers, optimizer, learning rate, and momentum factor for diagnosing pneumonia using CNN. In addition, we test different CNN models and augmentation methods for chest x-ray diagnosing. Experimental results show that the proposed non-rigid transform based on augmentation increases classification accuracy by up to 5%.
- KSP Keywords
- Chest X-ray, Classification Performance, Convolution neural network(CNN), Learning rate, Learning-Based classification, Momentum Factor, Number of layers, classification accuracy, deep learning(DL), hyper-parameters, non-rigid