IMV Medical Information Division. 2021 X-ray DR CR Market Outlook Report. https://imvinfo.com/product/2021-x-ray-dr-cr-market-outlook-report/ . Visited on 4th May, 2022.

Meena T, Roy S. Bone Fracture detection using deep supervised learning from radiological images: A paradigm shift. Diagnostics. 2022;12:2420. https://doi.org/10.3390/diagnostics12102420.

Article  PubMed  PubMed Central  Google Scholar 

Roy S, Meena T, Lim S-J. Demystifying supervised learning in healthcare 4.0: a new reality of transforming diagnostic medicine. Diagnostics. 2022;12(10):2549. https://doi.org/10.3390/diagnostics12102549.

Article  PubMed  PubMed Central  Google Scholar 

Satia I, Bashagha S, Bibi A, Ahmed R, Mellor S, Zaman F. Assessing the accuracy and certainty in interpreting chest X-rays in the medical division. Clinical medicine. 2013;13(4):349–52.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Weinstock Michael B, Md Ana Echenique, Md Dabr, Russell Joshua W, Md Msc, Facep Ari Leib, Md Jordan A, Miller Do, Cohen David J, Md Stephen Waite, Md Allen Frye, Np Andfrank A, Illuzzi,. Chest x-ray findings in 636 ambulatory patients with COVID-19 presenting to an urgent care center: a normal chest x-ray is no guarantee. J Urgent Care Med. 2020;14(7):13–8.

Google Scholar 

Chakraborty S, Kumar K, Reddy BP, Meena T, Roy S. An explainable AI based clinical assistance model for identifying patients with the onset of sepsis. Bellevue, WA, USA: 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI); 2023. p. 297–302. https://doi.org/10.1109/IRI58017.2023.00059.

Book  Google Scholar 

Çallı E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Medical Image Analysis. 2021;72: 102125.

Article  PubMed  Google Scholar 

Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW. Efficient pneumonia detection in chest xray images using deep transfer learning. Diagnostics. 2020;10(6):417.

Article  PubMed  PubMed Central  Google Scholar 

Hu M, Lin H, Fan Z, Gao W, Yang L, Liu C, Song Q. Learning to recognize chest-Xray images faster and more efficiently based on multi-kernel depthwise convolution. IEEE Access. 2020;8:37265–74.

Article  Google Scholar 

Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE CVPR; 2017. p. 2097–106.

Google Scholar 

Dufour JM, Neves J. Finite-sample inference and nonstandard asymptotics with Monte Carlo tests and R. In: Handbook of statistics, vol. 41. Elsevier; 2019. p. 3–31.

Google Scholar 

Xue Z, You D, Candemir S, Jaeger S, Antani S, Long LR, Thoma GR. Chest X-ray image view classification. Sao Carlos, Brazil: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems; 2015. p. 66–71. https://doi.org/10.1109/CBMS.2015.49.

Book  Google Scholar 

Kabiraj A, Meena T, Reddy PB, Roy S. Detection and classification of lung disease using deep learning architecture from x-ray images. In: International Symposium on Visual Computing, vol. 13598. Cham: Springer International Publishing; 2022. p. 444–55. https://doi.org/10.1007/978-3-031-20713-6_34.

Chapter  Google Scholar 

Meena T, Kabiraj A, Reddy PB, Roy S. Weakly supervised confidence aware probabilistic CAM multi-thorax anomaly localization network. In: 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI). IEEE; 2023. p. 309–14. https://doi.org/10.1109/IRI58017.2023.00061.

Chapter  Google Scholar 

Tabik S, Gomez-Rios A, Martin-Rodriguez JL, Sevillano-Garcia I, Rey-Area M, Charte D, Guirado E, Suarez JL, Luengo J, Valero-Gonzalez MA, Garcia-Villanova P, Olmedo-Sanchez E, Herrera F. COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. IEEE J Biomed Health Inform. 2020;24(12):3595–605. https://doi.org/10.1109/JBHI.2020.3037127. Epub 2020 Dec 4 PMID: 33170789.

Article  CAS  PubMed  Google Scholar 

Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence. 2021;51(2):854–64.

Article  PubMed  Google Scholar 

Alghamdi HS, Amoudi G, Elhag S, Saeedi K, Nasser J. Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey. IEEE Access. 2021;25(9):20235–54. https://doi.org/10.1109/ACCESS.2021.3054484. PMID:34786304;PMCID:PMC8545235.

Article  Google Scholar 

Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal. 2020;65:101794.

Article  PubMed  PubMed Central  Google Scholar 

Wu W, Gao L, Duan H, Huang G, Ye X, Nie S. Segmentation of pulmonary nodules in CT images based on 3D-UNET combined with three-dimensional conditional random field optimization. Medical Physics. 2020;47(9):4054–63.

Article  ADS  PubMed  Google Scholar 

Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A. Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recognition Letters. 2020;138:638–43.

Article  ADS  PubMed  PubMed Central  Google Scholar 

Zhang J, Xie Y, Li Y, Shen C, Xia Y. Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint; 2020. arXiv:2003.12338.

Yuan Z, Yan Y, Sonka M, Yang T. Large-scale robust deep auc maximization: a new surrogate loss and empirical studies on medical image classification. Proceedings of the IEEE/CVF International Conference on Computer Vision; 2021. p. 3040–9. arXiv:2012.03173.

Laube KA, Zell A. ShuffleNASNets: efficient CNN models through modified efficient neural architecture search. Budapest, Hungary: 2019 International Joint Conference on Neural Networks (IJCNN); 2019. p. 1–6. https://doi.org/10.1109/IJCNN.2019.8852294.

Book  Google Scholar 

Hesterman JY, Caucci L, Kupinski MA, Barrett HH, Furenlid LR. Maximum-likelihood estimation with a contracting-grid search algorithm. IEEE transactions on nuclear science. 2010;57(3):1077–84.

Article  ADS  PubMed  PubMed Central  Google Scholar 

National Institutes of Health - Clinical Center, ChestXray-NIHCC, Available at: https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345, (Accessed on 10 January 2022)

Kermany D, Zhang K, Goldbaum M. Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley Data. 2018;2(2):651. https://doi.org/10.17632/rscbjbr9sj.3.

Article  Google Scholar 

Gulli A, Pal S. Deep learning with Keras. Packt Publishing Ltd; 2017.

Google Scholar 

Sabottke CF, Spieler BM. The effect of image resolution on deep learning in radiography. Radiol Artif Intell. 2020;2(1):e190015.

Article  PubMed  PubMed Central  Google Scholar 

Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR; 2019. p. 6105–14.

Google Scholar 

Larsson G, Maire M, Shakhnarovich G. Fractalnet: ultra-deep neural networks without residuals. arXiv preprint; 2016. arXiv:1605.07648.

Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint; 2014. arXiv:1412.6980.

Cui J, Guo H, Wang H, Chen F, Shu L, Li LC. Fully automatic segmentation of coronary artery using growing algorithm. Journal of X-ray Science and Technology. 2020;28(6):1171–86.

Article  PubMed  Google Scholar 

Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. arXiv preprint; 2016. arXiv:1602.07360.

Chu Z, Tian T, Feng R, Wang L. Sea-land segmentation with Res-UNet And fully connected CRF. Yokohama, Japan: IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium; 2019. p. 3840–3. https://doi.org/10.1109/IGARSS.2019.8900625.

Book  Google Scholar 

Sarker L, Islam MM, Hannan T, Ahmed Z. COVID-DenseNet: a deep learning architecture to detect COVID-19 from chest radiology images. preprint. 2020;2020050151.