Abstract
One of the most common exams done in hospitals is the chest radiograph. From results of this exam, many illnesses can be diagnosed such as Pneumonia, which is deadliest illness for children. The main objective of this work is to propose a convolutional neural network model that performs the diagnosis of pneumonia through chest radiographs. The model’s proposed architecture is automatically generated through optimization of hyperparameters. Generated models were trained and validated with an image base of chest radiographs presenting cases of viral and bacterial pneumonia. The best architecture found resulted in an accuracy of 95.3% and an AUC of 94% for diagnosing pneumonia, while the best architecture for the classification of type of pneumonia attained an accuracy of 83.1% and AUC of 80%.
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Lodha, R., Kabra, S.K., Pandey, R.M.: Antibiotics for community-acquired pneumonia in children. Cochrane Database Syst. Rev. (6). Article No. CD004874 (2013). https://doi.org/10.1002/14651858.CD004874
World Health Organization. http://www.who.int/news-room/fact-sheets/detail/pneumonia. Accessed 15 Feb 2019
Save the Children Organization. https://reliefweb.int/report/world/pneumonia-kill-nearly-11-million-children-2030. Accessed 12 Feb 2019
Cerentini, A., et al.: Automatic identification of glaucoma using deep learning methods. In: Studies in Health Technology and Informatics, PubMed (2017)
Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 524, 115–118 (2014). https://doi.org/10.1038/nature21056
Oliveira, L.L.G., et al.: Computer-aided diagnosis in chest radiography for detection of childhood pneumonia. Int. J. Med. Inform. 77(8), 555–564 (2008). https://doi.org/10.1016/j.ijmedinf.2007.10.010
Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. In: Computing Research Repository, eprint 1711.05225, arXiv (2017)
Kermany, D., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122–1131 (2018). https://doi.org/10.1016/j.cell.2018.02.010
Wang, X., et al.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Computing Research Repository, eprint 1705.02315, arXiv (2018)
Bergstra, J, et al.: Algorithms for hyper-parameter optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, pp. 2546–2554. Curran Associates Inc., Granada (2011)
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Sousa, G.G.B., Fernandes, V.R.M., de Paiva, A.C. (2019). Optimized Deep Learning Architecture for the Diagnosis of Pneumonia Through Chest X-Rays. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_31
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DOI: https://doi.org/10.1007/978-3-030-27272-2_31
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