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DLLACC: Design of an Efficient Deep Learning Model for Identification of Lung Air Capacity in COPD Affected Patients

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ICT for Intelligent Systems ( ICTIS 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 361))

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Abstract

Chronic obstructive pulmonary disease, generally known as COPD, is a progressive and eventually deadly lung condition that may be caused by emphysema and other illnesses. To detect these issues early and increase the patient's chance of survival, it is strongly advocated that chest X-rays be processed using very powerful image processing models. The geometry of the alveoli is harmed as a result of the bronchioles being narrowed and mucus-clogged in COPD. Utilizing sophisticated classification techniques for images, such as convolutional neural networks, one may see these shifts (CNN). When diagnosing COPD illnesses, CNNs have shown an accuracy of more than 95% when compared to static datasets. This accuracy is significantly impacted by variations in patient age, input image quality, capture angle, visual distortions, dataset type, and other elements. This paper provides a deep transfer learning-based incremental learning method for gradually updating the classification weights in the system. This method’s objective is to maintain the CNNs’ dependability over time. After analysing it on three different sets of data collected from X-rays of the lungs, the researchers found that the recommended model correctly recognised COPD in 99.5% of the instances. The results of this research suggest that when doing temporal analysis of COPD-detected images and calculating a patient’s projected life expectancy, Gated Recurrent Units (GRUs) should be employed. Medical practitioners utilise a process called lifetime analysis, which might result in more harsh treatment methods. As a result of patients being advised against continuous chest X-rays because to the long-term negative effects, a smaller dataset was used for the temporal analysis of COPD values, which produced an accuracy of 97% for lifetime analysis. Patients were advised against routine chest X-rays owing to their negative long-term effects.

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References

  1. Rajasenbagam T et al (2021) Detection of pneumonia infection in lungs from chest X-Ray images using deep convolutional neural network and content-based image retrieval techniques. J Ambient Intell Humaniz Comput 2021:1–8. https://doi.org/10.1007/s12652-021-03075-2

    Article  Google Scholar 

  2. Chagas JV et al (2021) A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system. J Real-Time Image Proc 18(4):1099–1114. https://doi.org/10.1007/s11554-021-01086-y

    Article  Google Scholar 

  3. El Asnaoui K (2021) Design ensemble deep learning model for pneumonia disease classification. Int J Multimed Inform Retr 10(1):55–68. https://doi.org/10.1007/s13735-021-00204-7

    Article  Google Scholar 

  4. López-Cabrera JD et al (2021) Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging. Heal Technol 11(2):411–424. https://doi.org/10.1007/s12553-021-00520-2

    Article  Google Scholar 

  5. Rahman S et al (2021) Deep learning-driven automated detection of COVID-19 from radiography images: a comparative analysis. Cogn Comput 2021:1–30. https://doi.org/10.1007/s12559-020-09779-5

    Article  Google Scholar 

  6. Perumal V et al (2021) Detection of COVID-19 using CXR and CT images using transfer learning and haralick features. Appl Intell 51(1):341–358. https://doi.org/10.1007/s10489-020-01831-z

    Article  Google Scholar 

  7. https://www.webmd.com/lung/copd/covid-copd-overview/

  8. Qi X et al (2021) Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network. Int J Comput Assist Radiol Surg 16(2):197–206. https://doi.org/10.1007/s11548-020-02305-w

    Article  Google Scholar 

  9. Rashee J et al (2021) A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images. Interdiscip Sci Comput Life Sci 13(1):103–117. https://doi.org/10.1007/s12539-020-00403-6

    Article  Google Scholar 

  10. Ma J et al (2020) Survey on Deep Learning for Pulmonary Medical Imaging. Frontiers of Medicine 14(4):450–469. https://doi.org/10.1007/s11684-019-0726-4

    Article  Google Scholar 

  11. Sultan LR et al (2021) Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID-19. J Am College Emerg Phys Open 2(2):e12418. https://doi.org/10.1002/emp2.12418

    Article  Google Scholar 

  12. Zheng S et al (2021) A dual-attention V-network for pulmonary lobe segmentation in CT scans. IET Image Proc 15(8):1644–1654. https://doi.org/10.1049/ipr2.12133

    Article  Google Scholar 

  13. Irmak E (2021) COVID-19 disease severity assessment using CNN model. IET Image Proc 15(8):1814–1824. https://doi.org/10.1049/ipr2.12153

    Article  Google Scholar 

  14. Sukanya Doddavarapu VN et al (2021) Rotational invariant fractional derivative filters for lung tissue classification. IET Image Proc 15(10):2202–2212. https://doi.org/10.1049/ipr2.12188

    Article  Google Scholar 

  15. Klimeš F et al (2021) 3D Phase-resolved functional lung ventilation MR imaging in healthy volunteers and patients with chronic pulmonary disease. Magn Reson Med 85(2):912–925. https://doi.org/10.1002/mrm.28482

    Article  MathSciNet  Google Scholar 

  16. Roy S et al (2020) Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans Med Imaging 39(8):2676–2687. https://doi.org/10.1109/tmi.2020.2994459

    Article  Google Scholar 

  17. Anthimopoulos M et al (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imag 35(5):1207–1216. https://doi.org/10.1109/tmi.2016.2535865

  18. Joshua N, Stephen E et al (2021) 3D CNN with visual insights for early detection of lung cancer using gradient-weighted class activation. J Healthcare Eng 2021:6695518. https://doi.org/10.1155/2021/6695518

    Article  Google Scholar 

  19. https://www.sciencedirect.com/science/article/pii/S2210670720308076. Accessed 15 Feb 2023

  20. An F et al (2021) Medical image classification algorithm based on visual attention mechanism-MCNN. Oxid Med Cell Longev 2021:6280690. https://doi.org/10.1155/2021/6280690

    Article  Google Scholar 

  21. Salamh S, Ahmed B et al (2021) A study of a new technique of the CT scan view and disease classification protocol based on level challenges in cases of coronavirus disease. Radiol Res Pract 2021:5554408. https://doi.org/10.1155/2021/5554408

    Article  Google Scholar 

  22. Zak M, Krzyżak A (2020) Classification of lung diseases using deep learning models. In: Lecture notes in computer science. Springer International Publishing, pp 621–634. https://doi.org/10.1007/978-3-030-50420-5_47

  23. Muthazhagan B et al (2020) An enhanced computer-assisted lung cancer detection method using content based image retrieval and data mining techniques. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02123-7

  24. Trusculescu AA et al (2020) Deep learning in interstitial lung disease-how long until daily practice. Eur Radiol 30(11):6285–6292. https://doi.org/10.1007/s00330-020-06986-4

    Article  Google Scholar 

  25. Farhat H et al (2020) Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19. Mach Vis Appl 31(6):53. https://doi.org/10.1007/s00138-020-01101-5

    Article  Google Scholar 

  26. Elaziz MA et al (2020) New machine learning method for image-based diagnosis of COVID-19. PLoS ONE 15(6):e0235187. https://doi.org/10.1371/journal.pone.0235187

    Article  Google Scholar 

  27. Fan D-P et al (2020) Inf-Net: automatic COVID-19 lung infection segmentation from CT images. medRxiv. https://doi.org/10.1101/2020.04.22.20074948

  28. Qin R et al (2020) Fine-grained lung cancer classification from PET and CT images based on multidimensional attention mechanism. Complexity 2020:1–12. https://doi.org/10.1155/2020/6153657

    Article  Google Scholar 

  29. Gupta YK, Agrawal S (2021) A study of lung disease using image processing in big data environment. Mater Sci Eng 1022, 012030. https://doi.org/10.1088/1757-899x/1022/1/012030.

  30. Du R et al (2020) Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN. IEEE Access Pract Innov Open Solut 8:38907–38919. https://doi.org/10.1109/access.2020.2974617

    Article  Google Scholar 

  31. Munawar F et al (2020) Segmentation of lungs in chest x-ray image using generative adversarial networks. IEEE Access Pract Innov Open Solut 8:153535–153545. https://doi.org/10.1109/access.2020.3017915

    Article  Google Scholar 

  32. Li X et al (2021) A deep learning system that generates quantitative CT reports for diagnosing pulmonary tuberculosis. Appl Intell 51(6):4082–4093. https://doi.org/10.1007/s10489-020-02051-1

    Article  Google Scholar 

  33. Mlodzinski E et al (2020) Machine learning for pulmonary and critical care medicine: a narrative review. Pulm Therap 6(1):67–77. https://doi.org/10.1007/s41030-020-00110-z

    Article  Google Scholar 

  34. Shi H et al (2020) Multimodal lung tumor image recognition algorithm based on integrated convolutional neural network. Concurr Comput Pract Exp 32(21):e4965. https://doi.org/10.1002/cpe.4965

    Article  Google Scholar 

  35. Peng J et al (2020) A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators. Sci Rep 10(1):3118. https://doi.org/10.1038/s41598-020-60042-1

    Article  MathSciNet  Google Scholar 

  36. Mei X et al (2020) Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med 26(8):1224–1228. https://doi.org/10.1038/s41591-020-0931-3

    Article  Google Scholar 

  37. Hosseini MP et al (2012) Detection and severity scoring of chronic obstructive pulmonary disease using volumetric analysis of lung CT images. Iran J Radiol 9(1):22–27. https://doi.org/10.5812/iranjradiol.6759

    Article  Google Scholar 

  38. Liu C et al (2020) A fully automatic segmentation algorithm for CT lung images based on random forest. Med Phys 47(2):518–529. https://doi.org/10.1002/mp.13939

    Article  Google Scholar 

  39. Yamamoto S et al (2020) Pulmonary perfusion by chest digital dynamic radiography: comparison between breath-holding and deep-breathing acquisition. J Appl Clin Med Phys 21(11):247–255. https://doi.org/10.1002/acm2.13071

    Article  Google Scholar 

  40. Kirby M et al (2020) Inter- and intra-software reproducibility of computed tomography lung density measurements. Med Phys 47(7):2962–2969. https://doi.org/10.1002/mp.14130

    Article  Google Scholar 

  41. Hasse K et al (2020) Systematic feasibility analysis of performing elastography using reduced dose CT lung image pairs. Med Phys 47(8):3369–3375. https://doi.org/10.1002/mp.14112

    Article  Google Scholar 

  42. Ahuja S et al (2021) Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl Intell 51(1):571–585. https://doi.org/10.1007/s10489-020-01826-w

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Ramdeobaba College of Engineering and Management, Nagpur, 44013, India

    Sruthi Nair

Authors
  1. Sruthi Nair
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Correspondence to Sruthi Nair .

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Editors and Affiliations

  1. Hertfordshire Business School, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Jyoti Choudrie

  2. Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Parikshit N. Mahalle

  3. University Putra Malaysia, Serdang, Selangor, Malaysia

    Thinagaran Perumal

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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Nair, S. (2023). DLLACC: Design of an Efficient Deep Learning Model for Identification of Lung Air Capacity in COPD Affected Patients. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart Innovation, Systems and Technologies, vol 361. Springer, Singapore. https://doi.org/10.1007/978-981-99-3982-4_18

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