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All issues Volume 399 (2023) E3S Web Conf., 399 (2023) 09001 References
Open Access
Issue
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
Article Number 09001
Number of page(s) 8
Section Life Science
DOI https://doi.org/10.1051/e3sconf/202339909001
Published online 12 July 2023
  1. U.R. Abeyratne, V. Swarnkar, R. Triasih,& A. Setyati,Cough Sound Analysis-A new tool for diagnosing Pneumonia, In 2013 35thAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2013). [Google Scholar]
  2. B. Antin, J. Kravitz, & E. Martayan, Detecting pneumonia in chest X-Rays with supervised learning, Semantic scholar. Org (2017). [Google Scholar]
  3. E. Ayan, H.M. Ünver, Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) 1–5 (2019). [Google Scholar]
  4. R.E. Black, S. Cousens, H.L. Johnson et al., Global, Regional, and National Causes of Child Mortality in 2008: A Systematic Analysis. The Lancet, 375 (2010) [Google Scholar]
  5. S. Chakraborty, S. Aich,J.S. Sim & H.C. Kim, Detection of Pneumonia from Chest X-Rays using a Convolutional Neural Network Architecture. In international conference on future information & communication engineering, 1 (2019). [Google Scholar]
  6. D.J. Alapat, M.V. Menon, S. Ashok. A Review on Detection of Pneumonia in Chest X-ray Images Using Neural Networks. J Biomed Phys Eng., 12, 6 (2022). [Google Scholar]
  7. A. Esteva, B. Kuprel,R.A. Novoa,J. Ko,S.M. Swetter, H.M. Blau, & S. Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 (2017). [Google Scholar]
  8. T. Franquet, Imaging of community acquired pneumonia. Journal of thoracic imaging, 33, 5 (2018). [Google Scholar]
  9. M. Grewal, M.M. Srivastava, P. Kumar,& S. Varadarajan,.Radnet: Radiologist level accuracy using deep learning for hemorrhage detection in ct scans. In 2018 IEEE 15th (ISBI) International Symposium on Biomedical Imaging (2018). [Google Scholar]
  10. A. Oates, K. Halliday, A.C. Offiah, C. Landes, N. Stoodley, A. Jeanes, et al.Shortage of paediatric radiologists acting as an expert witness: position statement from the British Society of Paediatric Radiology (BSPR) National Working Group on Imaging in Suspected Physical Abuse (SPA) Clin Radiol. 74(7),496–502,(2016). Deep residual learning for image recognition. Proceedings of the 29th IEEE Conference on CVPR. Piscataway: IEEE Computer Society. [Google Scholar]
  11. K. He, X. Zhang, J. Sun & S. Ren, Deep residual learning for image recognition. In Proceedings of the IEEE (CVPR) conference on computer vision and pattern recognition, (2016). [Google Scholar]
  12. D.S. Kermany, M. Goldbaum, W. Cai, C.C. Valentim,H. Liang,S.L. Baxter &J. Dong. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172, 5 (2018). [Google Scholar]
  13. A. Krizhevsky, G.E. Hinton & I. Sutskever, ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems ANIPS (2012). [Google Scholar]
  14. Liang, G., & Zheng, L. (2019). A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Computer methods and programs in biomedicine, 104964. [PubMed] [Google Scholar]
  15. T.H. Pingale, & H.T. Patil, H.T. Analysis of Cough Sound for Pneumonia Detection Using Wavelet Transform and Statistical Parameters. In 2017 International Conference on Computing, Communication, Control, and Automation (ICCUBEA) (2017) [Google Scholar]
  16. P. Rajpurkar, A.Y. Hannun, M. Haghpanahi, C. Bourn& A.Y. Ng, A. Y. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836(2017). [Google Scholar]
  17. P. Rajpurkar, J.K. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, & M.P. Lungren. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017). [Google Scholar]
  18. J. Kumarnath& K. Batri, A novel parametric analysis of the performance dynamicity of optical network. Advances in Natural and Applied sciences 11 (2011). [Google Scholar]
  19. R. Siddiqi, Automated Pneumonia Diagnosis using a Customized Sequential Convolutional Neural Network. In Proceedings of the 2019 3rd (ICDLT) International Conference on Deep Learning Technologies (2019). [Google Scholar]
  20. Bhuvaneshwari C., Manjunathan A. Materials Today Proceedings, 21, 731–733 (2020) [CrossRef] [Google Scholar]
  21. C. Bhuvaneshwari, A. Manjunathan, Materials Today: Proceedings, 45, 1547–1551 (2021) [CrossRef] [Google Scholar]
  22. M. Ramkumar, A. Lakshmi, M.P. Rajasekaran, A. Manjunathan. Biomedical Signal Processing and Control. 76, 103639(2022) [CrossRef] [Google Scholar]

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