Abstract
Major advancement in the field of medical image science is mainly due to deep learning technology, and it has demonstrated good performance in numerous applications such as segmentation and registration. Using generative adversarial networks (GAN), this study provides an outstanding data augmentation technique for developing synthetic chest X-ray images of pneumonia victims. The proposed model first leverages standard data augmentation methodologies in combination with GANs in order to produce more data. The unparalleled chest X-ray descriptions of patients who suffer from pneumonia using a unique application of GANs are developed. The generated samples are used to train a deep convolutional neural network (DCNN) model to classify chest X-ray data. The performance metrics values of existent and synthetic images were also compared and calculated.
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References
José RJ, Brown JS (2016) Opportunistic bacterial, viral and fungal infections of the lung. Medicine 44(6):378–83
World Health Organization (2001) Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children. World Health Organization
Kallianos K, Mongan J, Antani S, Henry T, Taylor A, Abuya J, Kohli M (2019) How far have we come? Artificial intelligence for chest radiograph interpretation. Clin Radiol 74(5):338–345
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18(8):500–510
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz C, Shpanskaya K, Lungren MP (2017) Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225
Jain R, Nagrath P, Kataria G, Kaushik VS, Hemanth DJ (2020) Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement 1(165):108046
Kim J, Kim J, Han G, Rim C, Jo H (2020) Low-dose CT image restoration using generative adversarial networks. Inform Med Unlocked 1(21):100468
Frid-Adar M, Klang E, Amitai M, Goldberger J, Greenspan H (2018) Synthetic data augmentation using GAN for improved liver lesion classification. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) 2018 April 4. IEEE, pp 289–293
Dong X, Lei Y, Tian S, Wang T, Patel P, Curran WJ, Jani AB, Liu T, Yang X (2019) Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network. Radiother Oncol 141:192–199
Qian P, Xu K, Wang T, Zheng Q, Yang H, Baydoun A, Zhu J, Traughber B, Muzic RF (2020) Estimating CT from MR abdominal images using novel generative adversarial networks. J Grid Comput 18(2):211–226
Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW (2020) Efficient pneumonia detection in chest Xray images using deep transfer learning. Diagnostics 10(6):417
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst 27
Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst
Srivastav D, Bajpai A, Srivastava P. Improved classification for pneumonia detection using transfer learning with GAN based synthetic image augmentation. In: 2021 11th International conference on cloud computing, data science engineering (confluence) 2021 Jan 28. IEEE, pp 433–437
Nitz DA (2006) Tracking route progression in the posterior parietal cortex. Neuron 49(5):747–756
Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434
Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training GANs. Adv Neural Inf Process Syst 29
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Porkodi, S.P., Sarada, V., Maik, V. (2023). DCGAN for Data Augmentation in Pneumonia Chest X-Ray Image Classification. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P. (eds) Proceedings of International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 600. Springer, Singapore. https://doi.org/10.1007/978-981-19-8825-7_12
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DOI: https://doi.org/10.1007/978-981-19-8825-7_12
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