Abstract
Pneumonia disease is caused by viruses and bacteria which affect one or both lungs. It is the most dangerous disease that causes huge cancer death worldwide. Early detection of Pneumonia is the only way to improve a patient’s chance for survival. We can detect this disease from X-ray or computed tomography (CT) lung images using deep learning techniques. This research paper provides a solution to medical practitioners in predicting the impact of virus as high-risk, low-risk and medium-risk among the population being tested through various deep learning techniques such as convolutional neural networks, artificial neural network (ANN) and recurrent neural networks using long short-term memory cells. We observed 3000 CT images of Pneumonia confirmed patients and achieved the accuracy resulting 98–99%. The performance of the classifiers is evaluated using parameters such as confusion matrix, accuracy, F-measure, precision and recall. The results prove that deep learning affords a fitting tool for fast screening of Pneumonia and discovering high-risk patients and preventing them by providing suitable medical remedies.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Availability of data and materials
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Change history
22 August 2024
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s00500-024-10106-5
References
Abate A, Barra P, Barra S, Molinari C, Nappi M, Narducci F (2020) Clustering facial attributes: narrowing the path from soft to hard biometrics. IEEE Access 8:9037–9045. https://doi.org/10.1109/ACCESS.2019.2962010
Ariza HM, Mozo VR, Quintero HM (2018) Methodology for the Agile development of software based on a guide for the body of knowledge of scrum (SBOKTM Guide). Int J Appl Eng Res 13(14):11479–11483
Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M (202) Mapping the landscape of artificial intelligence applications against lung diseases, https://arxiv.org/abs/2003.11336
Ibm (2012) Manual CRISP-DM de Ibm Spss Modeler. 56. Armonk: IBM Corp
Jamshidi M, Lalbakhsh A, Mohamadzade B, Siahkamari H, Mousavi SMH (2019) A novel neural-based approach for design of microstrip letters’. AEU-Int J Electron Commun 110:152847
Jamshidi M, Lalbakhsh A, Lot S, Siahkamari H, Mohamadzade B, Jalilian J (2020) A neuro-based approach to designing a Wilkinson power divider. Int J RF Microw Comput Aided Eng 30(3):e22091
Mamra A, Sibghatullah AS, Ananta GP, Alazzam MB, Ahmed YH, Doheir M (2017) Theories and factors applied in investigating the user acceptance towards personal health records: review study. Int J Healthcare Manag 10(2):89–96. https://doi.org/10.1080/20479700.2017.1289439
Mesleh A (2017) Lung cancer detection using multi-layer neural networks with independent component analysis: a comparative study of training algorithms, Jordan J Biol Sci, 10
Pérez-Rodríguez E, Curto L, Arias F, Ladron de Guevara A, Bendito A (1986) Noninvasive evaluation of mediastinal metastasés in bronchogenic carcinoma with 67Ga scanning. Chest. https://doi.org/10.1378/chest.90.1.150
Rajaraman S, Candemir S, Thoma G, Antani S (2019) Medical Imaging 2019: Computer-Aided Diagnosis. 10950. Bellingham: SPIE; March 2019. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs; pp 200–211
Suzuki K (2017) Overview of deep learning in medical imaging. Radiol Phys Technol 10:257–273
Zhang F (2021) Application of machine learning in CT images and X-rays of COVID-19 pneumonia. Medicine. https://doi.org/10.1097/md.0000000000026855
Zhao H, Li G, Feng W (2018) Research on application of artificial intelligence in medical education, In: Proc Int Conf Eng Simulation Intell Control (ESAIC), Changsha, China, Aug. 8, 340_342
Funding
No funding was received to assist with the preparation of this manuscript.
Author information
Authors and Affiliations
Contributions
All authors are contributed equally.
Corresponding author
Ethics declarations
Conflict of interests
Not applicable.
Ethical approval
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s00500-024-10106-5
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Meena, K., Veeramakali, T., Singh, N.H. et al. RETRACTED ARTICLE: Deep learning techniques for prediction of pneumonia from lung CT images. Soft Comput 27, 8481–8491 (2023). https://doi.org/10.1007/s00500-023-08280-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-08280-z