The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare
Open accessAbstract
This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.
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Cites
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Cited by
Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538
“[43] proposed a new study of classifying and detect the presence of pneumonia from a collection of chest X-ray image samples based on a ConvNet model trained from scratch based on dataset [44].”
BackgroundToraman, S., Alakus, T. B., & Turkoglu, I. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110122 · Cited by 327
A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography
Ait Nasser, A., & Akhloufi, M. A. (2023) · Diagnostics · doi:10.3390/diagnostics13010159 · Cited by 121
Deep Ensemble Model for Classification of Novel Coronavirus in Chest X‐Ray Images
Ahmad, F., Farooq, A., & Ghani, M. U. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8890226 · Cited by 65
Metadata
- Authors
- Stephen, O., Sain, M., Maduh, U. J., & Jeong, D.-U.
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2019-03-27
- Volume
- 2019
- Pages
- 1-7
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- OpenAlex
- W2924911266
- Cited by
- 616
- References
- 17
- FWCI
- 47.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
