Deep Learning for Chest Radiographs by unknow

Author:unknow
Language: eng
Format: epub
Publisher: Elsevier Science & Technology
Published: 2021-07-15T00:00:00+00:00

Further reading

[78] Gu X., Pan L., Liang H., Yang R. Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography. In: Proceedings of the 3rd International Conference on Multimedia and Image Processing; 2018:88–93.

[79] Chowdhury M.E., Rahman T., Khandakar A., Mazhar R., Kadir M.A., Mahbub Z.B., Islam K.R., Khan M.S., Iqbal A., Al-Emadi N., Reaz M.B.I. Can AI help in screening viral and COVID-19 pneumonia? arXiv preprint arXiv:2003.13145. 2020.

[80] Toğaçar M., Ergen B., Cömert Z., Özyurt F. A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. IRBM. 2020;41(4):212–222.

[81] Li Z., Yu J., Li X., Li Y., Dai W., Shen L., Mou L., Pu Z. PNet: an efficient network for pneumonia detection. In: 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE; 2019:1–5.

[82] Sharma H., Jain J.S., Bansal P., Gupta S. Feature extraction and classification of chest X-ray images using CNN to detect pneumonia. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE; 2020:227–231.

[83] Saraiva A.A., Ferreira N.M.F., de Sousa L.L., Costa N.J.C., Sousa J.V.M., Santos D.B.S., Valente A., Soares S. Classification of images of childhood pneumonia using convolutional neural networks. In: BIOIMAGING. February, 2019:112–119.

[84] Liang G., Zheng L. A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput. Methods Prog. Biomed.. 2020;187:104964.

[85] Varshni D., Thakral K., Agarwal L., Nijhawan R., Mittal A. Pneumonia detection using CNN based feature extraction. In: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE; 2019:1–7.

[86] Cohen J.P., Morrison P., Dao L., Roth K., Duong T.Q., Ghassemi M. Covid-19 image data collection: prospective predictions are the future, arXiv preprint arXiv:2006.11988. 2020.

[87] Gadermayr M., Dombrowski A.K., Klinkhammer B.M., Boor P., Merhof D. CNN cascades for segmenting whole slide images of the kidney, arXiv preprint arXiv: 1708.00251. 2017.

[88] Makde V., Bhavsar J., Jain S., Sharma P. Deep neural network based classification of tumourous and non-tumorous medical images. In: International Conference on Information and Communication Technology for Intelligent Systems. Cham: Springer; 2017, March:199–206.

[89] Kolachalama V.B., Singh P., Lin C.Q., Mun D., Belghasem M.E., Henderson J.M., Francis J.M., Salant D.J., Chitalia V.C. Association of pathological fibrosis with renal survival using deep neural networks. Kidney Int. Rep.. 2018;3(2):464–475.

[90] Choi J.W., Ku Y., Yoo B.W., Kim J.A., Lee D.S., Chai Y.J., Kong H.J., Kim H.C. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PLoS One. 2017;12(12):e0189259.

[91] Glasmachers T. Limits of end-to-end learning, arXiv preprint arXiv:1704.08305. 2017.

[92] Bengio Y. Practical recommendations for gradient-based training of deep architectures. In: Neural Networks: Tricks of the Trade. Berlin, Heidelberg: Springer; 2012:437–478.

[93] Goodfellow I., Bengio Y., Courville A., Bengio Y. Deep Learning. (Vol. 1, No. 2) Cambridge: MIT Press; 2016.

[94] Oliveira D.A.B., Viana M.P. Lung nodule synthesis using cnn-based latent data representation. In: International Workshop on Simulation and Synthesis in Medical Imaging. Cham: Springer; 2018:111–118.

[95] Yang Q. An introduction to transfer learning. In: ADMA. October, 2008:1.

[96] Raghu M., Zhang C.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.