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Advancing Pulmonary Infection Diagnosis: A Comprehensive Review of Deep Learning Approaches in Radiological Data Analysis

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Abstract

Early detection of infectious lung diseases is vital, and various researchers have created models to help with this. Different experts may have different opinions about how to classify a particular image in the dataset. The expertise, level of experience, or personal preferences of the experts might be the source of these differences. Automatic disease classification can help radiologists by reducing workload and improving patient care. Recent advancements in deep learning have helped the diagnosis and classification of lung diseases in medical imaging. As a result, there are several research in the literature utilising deep learning to identify lung diseases. A comprehensive review of the most recent DL and ML methods for lung disease diagnosis is given in this work. The selected studies are published from 2019 until 2024. Overall, total seventy-seven carefully chosen papers from various publications, including Nature, IEEE, Springer, Elsevier, and Wiley, are included in this study. Deep learning techniques for the detection of infectious lung diseases from medical images are presented in this paper. In addition to providing a taxonomy of the most advanced deep learning and machine learning-based lung disease detection systems, this comprehensive review also seeks to identify existing challenges, present the trends in the field’s current research, and provide projections about potential future directions.

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References

  1. Shah PM, Zeb A, Shafi U, Zaidi SFA, Shah MA (2018) Detection of Parkinson disease in brain MRI using convolutional neural network. In: ICAC 2018—2018 24th IEEE international conference on automation and computing: improving productivity through automation and computing, September 2018. https://doi.org/10.23919/ICONAC.2018.8749023

  2. Hussain SS et al (2023) Classification of Parkinson’s disease in patch-based MRI of substantia Nigra. Diagnostics 13(17):2827. https://doi.org/10.3390/DIAGNOSTICS13172827

    Article  Google Scholar 

  3. Shah PM et al (2020) 2D-CNN based segmentation of ischemic stroke lesions in MRI scans. Commun Comput Inf Sci 1287:276–286. https://doi.org/10.1007/978-3-030-63119-2_23

    Article  Google Scholar 

  4. Khan H, Shah PM, Shah MA, ul Islam S, Rodrigues JJPC (2020) Cascading handcrafted features and convolutional neural network for IoT-enabled brain tumor segmentation. Comput Commun 153:196–207. https://doi.org/10.1016/J.COMCOM.2020.01.013

    Article  Google Scholar 

  5. Ghaffar Z et al (2022) Comparative analysis of state-of-the-art deep learning models for detecting COVID-19 lung infection from chest X-ray images. https://arxiv.org/abs/2208.01637v1. Accessed 13 Feb 2025

  6. Khan M et al (2023) IoMT-enabled computer-aided diagnosis of pulmonary embolism from computed tomography scans using deep learning. Sensors 23(3):1471. https://doi.org/10.3390/S23031471

    Article  Google Scholar 

  7. Agarwal P, Romano L, Prosch H, Schueller G (2017) Infection. Med Radiol. https://doi.org/10.1007/174_2016_38/FIGURES/51

    Article  Google Scholar 

  8. Gevenois A, Bankier A, Sibille Y, Franquet T (2001) Imaging of pneumonia: trends and algorithms. Eur Respir J 18:196–208

    Google Scholar 

  9. the-challenges-and-opprtunities-of-tomorrow-s-radiologist***

  10. Rahman T et al (2020) Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray. Appl Sci (Switzerland). https://doi.org/10.3390/APP10093233

    Article  Google Scholar 

  11. Serena Low WC et al (2021) An overview of deep learning techniques on chest X-ray and CT scan identification of COVID-19. Comput Math Methods Med. https://doi.org/10.1155/2021/5528144

    Article  Google Scholar 

  12. Graif M (2014) Volume 14-Issue 4, 2014-Management Matrix’***

  13. Chudgar P (2001) Pediatric radiology: pulmonary infections. https://www.pediatriconcall.com/articles/pediatric-radiology/pulmonary-infections-radiology/pulmonary-infections-radiology-introduction. Accessed 24 Jul 2023

  14. Chang CC, Cheng AC, Chang AB (2014) Over-the-counter (OTC) medications to reduce cough as an adjunct to antibiotics for acute pneumonia in children and adults. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD006088.PUB4/FULL

    Article  Google Scholar 

  15. National Institutes of Health (2023) Pneumonia—causes and risk factors | NHLBI, NIH. https://www.nhlbi.nih.gov/health/pneumonia/causes. Accessed 25 Jul 2023

  16. Safiri S et al (2022) Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990–2019: results from the Global Burden of Disease Study 2019. BMJ. https://doi.org/10.1136/BMJ-2021-069679

    Article  Google Scholar 

  17. Tao S, Kieu H, Bade A, Hanafi M, Hijazi A, Kolivand H (2020) A survey of deep learning for lung disease detection on medical images: state-of-the-art, taxonomy, issues and future directions. J Imaging 6(12):131. https://doi.org/10.3390/jimaging6120131

    Article  Google Scholar 

  18. Ard Vajda S et al (2018) Systems-level quality improvement feature selection for automatic tuberculosis screening in frontal chest radiographs. J Med Syst 42:146. https://doi.org/10.1007/s10916-018-0991-9

    Article  Google Scholar 

  19. Ortiz-Toro C, García-Pedrero A, Lillo-Saavedra M, Gonzalo-Martín C (2022) Automatic detection of pneumonia in chest X-ray images using textural features. Comput Biol Med 145:105466. https://doi.org/10.1016/J.COMPBIOMED.2022.105466

    Article  Google Scholar 

  20. Ling G, Cao C (2020) Automatic detection and diagnosis of severe viral pneumonia CT images based on LDA-SVM. IEEE Sens J 20(20):11927–11934. https://doi.org/10.1109/JSEN.2019.2959617

    Article  Google Scholar 

  21. Wang Q, Yang D, Li Z, Zhang X, Liu C (2020) Deep regression via multi-channel multi-modal learning for pneumonia screening. IEEE Access 8:78530–78541. https://doi.org/10.1109/ACCESS.2020.2990423

    Article  Google Scholar 

  22. Muhammad Y, Alshehri MD, Alenazy WM, Vinh Hoang T, Alturki R (2021) Identification of pneumonia disease applying an intelligent computational framework based on deep learning and machine learning techniques. Mobile Inf Syst. https://doi.org/10.1155/2021/9989237

    Article  Google Scholar 

  23. Kong L, Cheng J (2021) Based on improved deep convolutional neural network model pneumonia image classification. PLoS ONE 16(11):e0258804. https://doi.org/10.1371/JOURNAL.PONE.0258804

    Article  Google Scholar 

  24. Feng Y, Yang X, Qiu D, Zhang H, Wei D, Liu J (2021) PCXRNet: condense attention block and multiconvolution spatial attention block for pneumonia chest X-ray detection. https://www.techrxiv.org/articles/preprint/PCXRNet_Condense_attention_block_and_Multiconvolution_spatial_attention_block_for_Pneumonia_Chest_X-Ray_detection/14904837/files/28744302.pdf. Accessed 26 Jul 2023

  25. Singh S, Tripathi BK (2022) Pneumonia classification using quaternion deep learning. Multimed Tools Appl 81(2):1743–1764. https://doi.org/10.1007/S11042-021-11409-7

    Article  Google Scholar 

  26. Kundu R, Das R, Geem ZW, Han GT, Sarkar R (2021) Pneumonia detection in chest X-ray images using an ensemble of deep learning models. PLoS ONE. https://doi.org/10.1371/journal.pone.0256630

    Article  Google Scholar 

  27. Harshvardhan GM, Gourisaria MK, Rautaray SS, Pandey M (2021) Pneumonia detection using CNN through chest X-ray. J Eng Sci Technol (JESTEC) 16(1):861–876

    Google Scholar 

  28. Yaseliani M, Hamadani AZ, Maghsoodi AI, Mosavi A (2022) Pneumonia detection proposing a hybrid deep convolutional neural network based on two parallel visual geometry group architectures and machine learning classifiers. IEEE Access 10:62110–62128. https://doi.org/10.1109/access.2022.3182498

    Article  Google Scholar 

  29. Varshni D, Thakral K, Agarwal L, Nijhawan R, Mittal A (2019) Pneumonia detection using CNN based feature extraction. In: IEEE international conference on electrical, computer and communication technologies (ICECCT). https://ieeexplore.ieee.org/abstract/document/8869364/. Accessed 26 Jul 2023

  30. Wang F et al (2023) Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography. Eur Radiol 33:8869–8878. https://doi.org/10.1007/s00330-023-09833-4

    Article  Google Scholar 

  31. Ukwuoma CC, Qin Z, Heyat MBB, Akhtar F, Bamisile O, Muaad AY, Addo D, Al-Antari MA (2023) A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res 48:191–211

    Google Scholar 

  32. Ali M et al (2024) ‘Pneumonia detection using chest radiographs with novel EfficientNetV2L model. IEEE Access 12:34691–34707. https://doi.org/10.1109/ACCESS.2024.3372588

    Article  Google Scholar 

  33. Wu L et al (2024) Pneumonia detection based on RSNA dataset and anchor-free deep learning detector. Sci Rep 14(1):1–8. https://doi.org/10.1038/s41598-024-52156-7

    Article  MathSciNet  Google Scholar 

  34. Parthasarathy V, Saravanan S (2024) Chaotic sea horse optimization with deep learning model for lung disease pneumonia detection and classification on chest X-ray images. Multimed Tools Appl 83(27):69825–69847. https://doi.org/10.1007/S11042-024-18301-0/FIGURES/15

    Article  Google Scholar 

  35. Teelhawod BN et al (2021) Machine learning in E-health: a comprehensive survey of anxiety. In: 2021 International conference on data analytics for business and industry, ICDABI 2021, pp 167–172. https://doi.org/10.1109/ICDABI53623.2021.9655966

  36. Badawi A, Elgazzar K (2021) ‘Detecting coronavirus from chest X-rays using transfer learning. COVID 1(1):403–415. https://doi.org/10.3390/COVID1010034

    Article  Google Scholar 

  37. Hussain L et al (2020) Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. Biomed Eng Online. https://doi.org/10.1186/s12938-020-00831-x

    Article  Google Scholar 

  38. Wang D, Mo J, Zhou G, Xu L, Liu Y (2020) An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PLoS ONE. https://doi.org/10.1371/JOURNAL.PONE.0242535

    Article  Google Scholar 

  39. Jin C et al (2020) Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 11(1):5088. https://doi.org/10.1038/s41467-020-18685-1

    Article  Google Scholar 

  40. Chen J et al (2020) Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep 10(1):1–11. https://doi.org/10.1038/s41598-020-76282-0

    Article  Google Scholar 

  41. Chowdhury M, Rahman T et al (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665–132676. https://doi.org/10.1109/ACCESS.2020.3010287

    Article  Google Scholar 

  42. Khuzani AZ, Heidari M, Shariati SS (2021) COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci Rep 11(1):9887. https://doi.org/10.1038/s41598-021-88807-2

    Article  Google Scholar 

  43. Rasheed J, Hameed AA, Djeddi C, Jamil A, Al-Turjman F (2021) A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci 13(1):103–117. https://doi.org/10.1007/s12539-020-00403-6

    Article  Google Scholar 

  44. Soda P et al (2021) AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. Med Image Anal 74:102216. https://doi.org/10.1016/J.MEDIA.2021.102216

    Article  Google Scholar 

  45. Wang G, Liu X, Shen J, Wang C, Li Z, Ye L, Wu X, Chen T, Wang K, Zhang X, Zhou Z, Yang J (2021) A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images. Nat Biomed Eng 5(6):509–521. https://doi.org/10.1038/s41551-021-00704-1

    Article  Google Scholar 

  46. Ohata EF et al (2021) Automatic detection of COVID-19 infection using chest X-ray images through transfer learning. IEEE/CAA J Autom Sin 8(1):239–248. https://doi.org/10.1109/JAS.2020.1003393

    Article  Google Scholar 

  47. Shiri I et al (2022) COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. Int J Imaging Syst Technol 32(1):12–25. https://doi.org/10.1002/IMA.22672

    Article  Google Scholar 

  48. Hossein Barshooi A, Amirkhani A (2022) A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-ray images. Biomed Signal Process Control 72:103326. https://doi.org/10.1016/j.bspc.2021.103326

    Article  Google Scholar 

  49. Wang Y, Rasheed J (2022) Analyzing the effect of filtering and feature-extraction techniques in a machine learning model for identification of infectious disease using radiography imaging. Symmetry 14(7):1398. https://doi.org/10.3390/SYM14071398

    Article  Google Scholar 

  50. Duong LT, Nguyen PT, Iovino L, Flammini M (2023) Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning. Appl Soft Comput 132:109851. https://doi.org/10.1016/j.asoc.2022.109851

    Article  Google Scholar 

  51. Issahaku FY, Liu X, Lu K, Fang X, Danwana SB, Asimeng E (2024) Multimodal deep learning model for Covid-19 detection. Biomed Signal Process Control 91:105906. https://doi.org/10.1016/J.BSPC.2023.105906

    Article  Google Scholar 

  52. Talukder MA, Layek MA, Kazi M, Uddin MA, Aryal S (2024) Empowering COVID-19 detection: optimizing performance through fine-tuned EfficientNet deep learning architecture. Comput Biol Med 168:107789. https://doi.org/10.1016/J.COMPBIOMED.2023.107789

    Article  Google Scholar 

  53. Ju H, Cui Y, Su Q, Juan L, Manavalan B (2024) CODENET: A deep learning model for COVID-19 detection. Comput Biol Med 171:108229. https://doi.org/10.1016/J.COMPBIOMED.2024.108229

    Article  Google Scholar 

  54. Wang L, Lin ZQ, Wong A (2020) COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci Rep 10(1):1–12. https://doi.org/10.1038/s41598-020-76550-z

    Article  Google Scholar 

  55. Cohen JP, Morrison P, Dao L, Roth K, Duong T, Ghassem M (2020) COVID-19 image data collection: prospective predictions are the future. Mach Learn Biomed Imaging 1:1–38. https://doi.org/10.59275/j.melba.2020-48g7

    Article  Google Scholar 

  56. Soares E, Angelov P, Biaso S, Froes MH, Abe DK (2020) SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv. https://doi.org/10.1101/2020.04.24.20078584

    Article  Google Scholar 

  57. Zhang K et al (2020) Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181(6):1423-1433.e11. https://doi.org/10.1016/j.cell.2020.04.045

    Article  Google Scholar 

  58. Talukder MA, Islam MM, Uddin MA, Akhter A (2022) COVID19 XRAY data. Mendeley Data. https://doi.org/10.17632/pvyh8j6xhn.1

    Article  Google Scholar 

  59. Talukder MA (2023) Chest X-ray image. Mendeley Data. https://doi.org/10.17632/M4S2JN3CSB.1

    Article  Google Scholar 

  60. Röhrich S, Schlegl T, Bardach C, Prosch H, Langs G (2020) Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography. Eur Radiol Exp 4(1):1–11. https://doi.org/10.1186/S41747-020-00152-7/FIGURES/8

    Article  Google Scholar 

  61. Malhotra P, Gupta S, Koundal D, Zaguia A, Kaur M, Lee HN (2022) deep learning-based computer-aided pneumothorax detection using chest X-ray images. Sensors 22(6):2278. https://doi.org/10.3390/S22062278

    Article  Google Scholar 

  62. Kitamura G, Deible C (2020) Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images. Clin Imaging 61:15–19. https://doi.org/10.1016/J.CLINIMAG.2020.01.008

    Article  Google Scholar 

  63. Gooßen A et al (2019) Deep learning for pneumothorax detection and localization in chest radiographs. https://arxiv.org/abs/1907.07324v1. Accessed 9 Aug 2023

  64. Sze-To A, Riasatian A, Tizhoosh HR (2021) Searching for pneumothorax in x-ray images using autoencoded deep features. Sci Rep 11(1):1–13. https://doi.org/10.1038/s41598-021-89194-4

    Article  Google Scholar 

  65. Li X et al (2019) Deep learning-enabled system for rapid pneumothorax screening on chest CT. Eur J Radiol 120:108692. https://doi.org/10.1016/J.EJRAD.2019.108692

    Article  Google Scholar 

  66. Gourisaria MK, Singh V, Chatterjee R, Panda SK, Pradhan MR, Acharya B (2023) PneuNetV1: a deep neural network for classification of pneumothorax using CXR images. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3289842

    Article  Google Scholar 

  67. Manikandan J, Shyni SA, Dhanalakshmi R, Akshaya SV, Dharshini S (2023) Segmentation and detection of pneumothorax using deep learning. In: International conference on ENT computing and control systems (ICICCS), pp 468–473. https://doi.org/10.1109/ICICCS56967.2023.10142364

  68. Singh R, Sharma A, Sharma N, Gupta R (2023) Xception model for pneumothorax classification using chest X-ray Images. In: 2023 2nd International conference for innovation in technology, INOCON 2023. https://doi.org/10.1109/INOCON57975.2023.10101280

  69. Lin FCF, Wei CJ, Bai ZR, Chang CC, Chiu MC (2024) Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax. Phys Med Biol 69(14):145017. https://doi.org/10.1088/1361-6560/AD5E31

    Article  Google Scholar 

  70. Huang H, Wang Q, Wang L (2024) Research on pneumothorax classification model of DenseNet based on multilayer network optimization. J Math 2024(1):8899192. https://doi.org/10.1155/2024/8899192

    Article  Google Scholar 

  71. SIIM-ACR pneumothorax Segmentation. Kaggle. https://www.kaggle.com/competitions/siim-acr-pneumothorax-segmentation. Accessed 11 Dec 2024

  72. Christe A et al (2019) Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol 54(10):627. https://doi.org/10.1097/RLI.0000000000000574

    Article  Google Scholar 

  73. Zucker EJ et al (2020) Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis. J Cyst Fibros 19(1):131–138. https://doi.org/10.1016/J.JCF.2019.04.016

    Article  Google Scholar 

  74. Syed AH, Khan T, Khan SA (2023) Deep transfer learning techniques-based automated classification and detection of pulmonary fibrosis from chest CT images. Processes 11(2):443. https://doi.org/10.3390/PR11020443/S1

    Article  Google Scholar 

  75. Wong A et al (2021) Fibrosis-Net: a tailored deep convolutional neural network design for prediction of pulmonary fibrosis progression from chest CT images. Front Artif Intell 4:764047. https://doi.org/10.3389/FRAI.2021.764047/BIBTEX

    Article  Google Scholar 

  76. Yadav A, Saxena R, Kumar A, Walia TS, Zaguia A, Kamal SMM (2022) FVC-NET: an automated diagnosis of pulmonary fibrosis progression prediction using honeycombing and deep learning. Comput Intell Neurosci. https://doi.org/10.1155/2022/2832400

    Article  Google Scholar 

  77. Liu Q et al (2022) Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population. BMC Pulm Med 22(1):1–11. https://doi.org/10.1186/S12890-022-02124-6/FIGURES/6

    Article  Google Scholar 

  78. Nishikiori H et al (2023) Deep-learning algorithm to detect fibrosing interstitial lung disease on chest radiographs. Eur Respir J. https://doi.org/10.1183/13993003.02269-2021

    Article  Google Scholar 

  79. Ukita J et al (2024) Detection of fibrosing interstitial lung disease-suspected chest radiographs using a deep learning-based computer-aided detection system: a retrospective, observational study. BMJ Open 14(1):e078841. https://doi.org/10.1136/BMJOPEN-2023-078841

    Article  Google Scholar 

  80. Yadav O, Passi K, Jain CK (2019) Using deep learning to classify X-ray images of potential tuberculosis patients. In: Proceedings—2018 IEEE international conference on bioinformatics and biomedicine, BIBM 2018, pp 2368–2375. https://doi.org/10.1109/BIBM.2018.8621525

  81. Jacutprakart J, Abolghasemi V, Andritsch J (2022) Ensemble deep learning architectures for automated diagnosis of pulmonary tuberculosis using chest X-ray. Interactive monitoring for tele-rehabilitation on mobile application and developing a system for healthcare analysis by using big data for osteoarthritis patients. ImageCLEF View project. https://doi.org/10.36227/techrxiv.21543309

  82. Qin ZZ et al (2019) Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep 9(1):1–10. https://doi.org/10.1038/s41598-019-51503-3

    Article  Google Scholar 

  83. Abideen ZU, Ghafoor M, Munir K, Saqib M, Ullah A, Zia T, Tariq SA, Ahmed G, Zahra A (2020) Uncertainty assisted robust tuberculosis identification with Bayesian convolutional neural networks. IEEE Access 8:22812–22825. https://doi.org/10.1109/ACCESS.2020.2970023

    Article  Google Scholar 

  84. Rajpurkar P et al (2020) CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest X-rays in patients with HIV. npj Digit Med 3(1):1–8. https://doi.org/10.1038/s41746-020-00322-2

    Article  Google Scholar 

  85. Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Hamid T, Islam MT (2020) Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3031384

    Article  Google Scholar 

  86. Tavaziva G, Majidulla A, Nazish A et al (2022) Diagnostic accuracy of a commercially available, deep learning-based chest X-ray interpretation software for detecting culture-confirmed pulmonary. Int J Infect Dis 122:15–20. https://doi.org/10.1016/j.ijid.2022.05.037

    Article  Google Scholar 

  87. Acharya V et al (2022) AI-assisted tuberculosis detection and classification from chest X-rays using a deep learning normalization-free network model. Comput Intell Neurosci. https://doi.org/10.1155/2022/2399428

    Article  Google Scholar 

  88. Mohan R, Kadry S, Rajinikanth V, Majumdar A, Thinnukool O (2022) Automatic detection of tuberculosis using VGG19 with Seagull-algorithm. Life 12(11):1848. https://doi.org/10.3390/LIFE12111848

    Article  Google Scholar 

  89. Fati SM, Senan EM, ElHakim N (2022) Deep and hybrid learning technique for early detection of tuberculosis based on X-ray images using feature fusion. Appl Sci 12(14):7092. https://doi.org/10.3390/APP12147092

    Article  Google Scholar 

  90. Showkatian E, Salehi M, Ghaffari H, Reiazi R, Sadighi N (2022) Deep learning-based automatic detection of tuberculosis disease in chest X-ray images. Pol J Radiol 87(1):118–124. https://doi.org/10.5114/PJR.2022.113435

    Article  Google Scholar 

  91. Kotei E, Thirunavukarasu R (2022) Ensemble technique coupled with deep transfer learning framework for automatic detection of tuberculosis from chest X-ray radiographs. Healthcare 10(11):2335. https://doi.org/10.3390/HEALTHCARE10112335

    Article  Google Scholar 

  92. Trong VO, Lin C-M (2023) An improved DenseNet deep neural network model for tuberculosis detection using chest X-ray images. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3270774

    Article  Google Scholar 

  93. Olayemi Alebiosu D, Dharmaratne A, Hong Lim C (2023) Improving tuberculosis severity assessment in computed tomography images using novel DAvoU-Net segmentation and deep learning framework. Expert Syst Appl 213:119287. https://doi.org/10.1016/J.ESWA.2022.119287

    Article  Google Scholar 

  94. Roopa NK, Mamatha GS (2024) CLBO: chef leader-based optimization enabled deep learning for tuberculosis detection using x-ray images. Signal Image Video Process 18(1):877–887. https://doi.org/10.1007/S11760-023-02732-7/TABLES/2

    Article  Google Scholar 

  95. Chen CF et al (2024) A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography. Sci Rep 14(1):1–10. https://doi.org/10.1038/s41598-024-65703-z

    Article  Google Scholar 

  96. Tuberculosis (TB) Chest X-ray Database. Kaggle. https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset. Accessed 30 Jan 2025

  97. Venkatesh C, Ramana K, Lakkisetty SY, Band SS, Agarwal S, Mosavi A (2022) A neural network and optimization based lung cancer detection system in CT images. Front Public Health 10:769692. https://doi.org/10.3389/FPUBH.2022.769692/BIBTEX

    Article  Google Scholar 

  98. Nasrullah N, Sang J, Alam MS, Mateen M, Cai B, Hu H (2019) Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors 19(17):3722. https://doi.org/10.3390/S19173722

    Article  Google Scholar 

  99. Feng J, Jiang J (2022) Deep learning-based chest CT image features in diagnosis of lung cancer. Comput Math Methods Med. https://doi.org/10.1155/2022/4153211

    Article  Google Scholar 

  100. Marappan S, Mujib MD, Siddiqui AA, Aziz A, Khan S, Singh M (2022) Lightweight deep learning classification model for identifying low-resolution CT images of lung cancer. Comput Intell Neurosci. https://doi.org/10.1155/2022/3836539

    Article  Google Scholar 

  101. Heuvelmans MA et al (2021) Lung cancer prediction by deep learning to identify benign lung nodules. Lung Cancer 154:1–4. https://doi.org/10.1016/J.LUNGCAN.2021.01.027

    Article  Google Scholar 

  102. Zhang C et al (2019) Toward an expert level of lung cancer detection and classification using a deep convolutional neural network. Oncologist 24(9):1159–1165. https://doi.org/10.1634/THEONCOLOGIST.2018-0908

    Article  Google Scholar 

  103. Shah AA, Malik HAM, Muhammad AH, Alourani A, Butt ZA (2023) Deep learning ensemble 2D CNN approach towards the detection of lung cancer. Sci Rep 13(1):1–15. https://doi.org/10.1038/s41598-023-29656-z

    Article  Google Scholar 

  104. Wankhade S (2023) A novel hybrid deep learning method for early detection of lung cancer using neural networks. Healthc Anal 3:100195. https://doi.org/10.1016/j.health.2023.100195

    Article  Google Scholar 

  105. Siddiqui EA, Chaurasia V, Shandilya M (2023) Detection and classification of lung cancer computed tomography images using a novel improved deep belief network with Gabor filters. Chemom Intell Lab Syst 235:104763. https://doi.org/10.1016/J.CHEMOLAB.2023.104763

    Article  Google Scholar 

  106. Prasad U, Chakravarty S, Mahto G (2024) Lung cancer detection and classification using deep neural network based on hybrid metaheuristic algorithm. Soft Comput 28(15–16):8579–8602. https://doi.org/10.1007/S00500-023-08845-Y/TABLES/11

    Article  Google Scholar 

  107. Shah PM et al (2022) DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection. Expert Syst 39(3):e12823. https://doi.org/10.1111/EXSY.12823

    Article  Google Scholar 

  108. Zargari Khuzani A, Heidari M, Shariati SA (2021) COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Sci Rep 11(1):1–6. https://doi.org/10.1038/s41598-021-88807-2

    Article  Google Scholar 

  109. Johnson AEW et al (2019) MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6(1):317. https://doi.org/10.1038/s41597-019-0322-0

    Article  Google Scholar 

  110. Irvin J et al (2019) Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI conference on artificial intelligence, 2019. https://ojs.aaai.org/index.php/AAAI/article/view/3834. Accessed 13 Sep 2023

  111. Bhandari M, Shahi T, Siku B, Neupane A (2022) Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Comput Biol Med 150:106156

    Google Scholar 

  112. Rahman T et al (2020) Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access 8:191586–191601. https://doi.org/10.1109/ACCESS.2020.3031384

    Article  Google Scholar 

  113. Armato SG et al (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931. https://doi.org/10.1118/1.3528204

    Article  Google Scholar 

  114. Nguyen D et al (2021) Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability. Front Artif Intell 4:694875. https://doi.org/10.3389/FRAI.2021.694875/BIBTEX

    Article  Google Scholar 

  115. Huy VTQ, Lin C-M (2023) An improved DenseNet deep neural network model for tuberculosis detection using chest X-ray images. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3270774

    Article  Google Scholar 

  116. Beigelman-Aubry C, Schmidt S (2016) Pulmonary infections: imaging with CT. Med Radiol. https://doi.org/10.1007/978-3-319-30355-0_8/FIGURES/50

    Article  Google Scholar 

  117. Walker CM, Abbott GF, Greene RE, Shepard J-AO, Vummidi D, Digumarthy SR (2014) Imaging pulmonary infection: classic signs and patterns. AJR. https://doi.org/10.2214/AJR.13.11463

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Sapna Yadav & Syed Afzal Murtaza Rizvi

  2. School of Engineering and Technology, K R Mangalam University, Gurugram, India

    Pankaj Agarwal

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  1. Sapna Yadav
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  2. Syed Afzal Murtaza Rizvi
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  3. Pankaj Agarwal
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Contributions

Ms. Sapna Yadav (Primary Author) conducted an extensive review of current literature on deep learning applications in pulmonary infection diagnosis using radiological data mainly X-ray, CT images. She synthesized findings from over 66 peer-reviewed articles to provide a comprehensive overview of the field. Ms. Sapna was responsible for drafting the initial manuscript, including the introduction, methodology, and other sections. She also coordinated the integration of contributions from co-authors and managed multiple rounds of revisions based on their feedback. She provided detailed analysis and comparisons of different approaches, highlighting their strengths, limitations. Dr. Syed Afzal Murtaza Rizvi (Co-Author) ensured the overall quality of the review paper. Dr. Pankaj Agarwal (Co-Author) provided expert insights into the technical aspects of deep learning methodologies. He detailed the architecture of AI system used in radiological image analysis. He contributed detailed explanations of how deep learning algorithms process and analyze radiological images. These contributions reflect a collaborative effort to produce a comprehensive and insightful review paper on the advancements in pulmonary infection diagnosis using deep learning approaches in radiological data analysis.

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Correspondence to Sapna Yadav.

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Yadav, S., Rizvi, S.A.M. & Agarwal, P. Advancing Pulmonary Infection Diagnosis: A Comprehensive Review of Deep Learning Approaches in Radiological Data Analysis. Arch Computat Methods Eng 32, 3759–3786 (2025). https://doi.org/10.1007/s11831-025-10253-4

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