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Intelligent prognostic system for pediatric pneumonia based on sustainable IoHT

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Abstract

Despite the growing impacts of environmental changes due to smart city development, sustainable Internet of Health Things (IoHT) retains improved public health. Containment of contagious diseases is one of the prime factors as the population density in the smart city environment is growing exponentially. This work focuses on the prognosis of pediatric pneumonia through the IoHT framework. In the world population, nearly 15% of children under five years of mortality are caused by a lung infection called pediatric pneumonia. It kills approximately 800 thousand children every year, and 2200 children daily mortality rate due to pediatric pneumonia. The disease is caused by viral or bacterial infections in the lungs. Chest X-ray (CXR) is the predominant method for diagnosing and severity analysis of pneumonia by pediatricians. However, the CXR images are low-quality images, demanding the intelligence for accurate analysis and interpretation. Hence, researchers developed different machine learning and deep learning methods to diagnose pneumonia from CXR images in recent years. However, it lacks the accuracy of interpretations. This paper proposes a deep transfer learning-based neural network-based IoHT framework to diagnose pneumonia due to viral and bacterial infections. The proposed model is twofold: the first is the deep transfer learning network for discriminating normal CXR from pneumonia-affected lung CXR images. The second is that the deep transfer learning network is trained by an optimized training method called Adaptive Movement Estimation and deployed in IoHT. The performance of the proposed system is analyzed in terms of accuracy, sensitivity, specificity, and Area Under the Curve (AUC). It yields the highest sensitivity of 98.2% and a precision of 98.8%. The proposed system also yields a validation accuracy of 97.88, which is high compared to other state-of-the-art transfer learning methods for diagnosing pediatric pneumonia.

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References

  1. Ambita AAE, Boquio ENV, Naval PC (2020) Locally adaptive regression kernels and support vector machines for the detection of pneumonia in chest X-ray images. In Asian Conference on Intelligent Information and Database Systems, Springer, Cham, pp 129–140

  2. Ayan E, Ünver HM (2019) Diagnosis of pneumonia from chest x-ray images using deep learning. In 2019 Sci Meet Electr-Electron Biomed Eng Comput Sci (EBBT), Ieee, pp 1–5

  3. Ayan E, Karabulut B, Ünver HM (2022) Diagnosis of pediatric pneumonia with ensemble of deep convolutional neural networks in chest X-ray images. Arab J Sci Eng 47(2):2123–2139

  4. Bada C, Carreazo NY, Chalco JP, Huicho L (2007) Inter-observer agreement in interpreting chest X-rays on children with acute lower respiratory tract infections and concurrent wheezing. Sao Paulo Med J 125:150–154

    Article  Google Scholar 

  5. Bhatt R, Yadav S, Sarvaiya JN (2020) Convolutional Neural Network Based Chest X-Ray Image Classification for Pneumonia Diagnosis. In International Conference on Emerging Technology Trends in Electronics Communication and Networking, Springer, Singapore, pp 254–266

  6. Bhuiyan MU, Snelling TL, West R, Lang J, Rahman T, Borland ML, Thornton R et al (2018) Role of viral and bacterial pathogens in causing pneumonia among Western Australian children: a case-control study protocol. BMJ Open 8, no. 3

  7. Chagas D, João Victor S, de Rodrigues DA, Ivo RF, Hassan MM, de Albuquerque VHC, Rebouças Filho PP (2021) A new approach for the detection of pneumonia in children using CXR images based on a real-time IoT system. J Real-Time Image Process, pp 1–16

  8. Cherian T, Kim Mulholland E, Carlin JB, Ostensen H, Amin R, de Campo M, Greenberg D et al (2005) Standardized interpretation of pediatric chest radiographs for diagnosing pneumonia in epidemiological studies. Bull World Health Organ 83:353–359

    Google Scholar 

  9. Chhikara P, Singh P, Gupta P, Bhatia T (2020) Deep convolutional neural network with transfer learning for detecting pneumonia on chest X-rays. In Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals, Springer, Singapore, pp 155–168

  10. Dey N, Yu-Dong Z, Rajinikanth V, Pugalenthi R, Raja NSM (2021) Customized VGG19 architecture for pneumonia detection in chest X-rays. Pattern Recogn Lett 143:67–74

    Article  Google Scholar 

  11. Dong L, Hu S, Gao J (2020) Discovering drugs to treat coronavirus disease 2019 (COVID-19). Drug Discov Ther 14(1):58–60

  12. Elshennawy NM, Ibrahim DM (2020) Deep-pneumonia framework using deep learning models based on chest X-ray images. Diagnostics 10(9):649

    Article  Google Scholar 

  13. Enwere G, Cheung YB, Zaman SMA, Akano A, Oluwalana C, Brown O, Vaughan A, Adegbola R, Greenwood B, Cutts F (2007) Epidemiology and clinical features of pneumonia according to radiographic findings in Gambian children. Tropic Med Int Health 12(11):1377–1385

    Article  Google Scholar 

  14. Fernandes V, Junior GB, de Paiva AC, Silva AC, Gattass M (2021) Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis. Comput Methods Prog Biomed 208:106259

    Article  Google Scholar 

  15. Habib N, Hasan MM, Reza M, Rahman MM (2020) Ensemble of CheXNet and VGG-19 feature extractor with random Forest classifier for pediatric pneumonia detection. SN Comput Sci 1(6):1–9

    Article  Google Scholar 

  16. Hashmi MF, Katiyar S, Keskar AG, Bokde ND, Geem ZW (2020) Efficient pneumonia detection in chest x-ray images using deep transfer learning. Diagn 10(6):417

    Article  Google Scholar 

  17. Hazir T, Nisar YB, Qazi SA, Khan SF, Raza M, Zameer S, Masood SA (2006) Chest radiography in children aged 2–59 months diagnosed with non-severe pneumonia as defined by World Health Organization: a descriptive multicentre study in Pakistan. BMJ 333(7569):629

    Article  Google Scholar 

  18. Jain R, Nagrath P, Kataria G, Kaushik VS, Hemanth DJ (2020) Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Meas 165:108046

    Article  Google Scholar 

  19. Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, Rodrigues JJPC (2019) Identifying pneumonia in chest X-rays: a deep learning approach. Measurement 145:511–518

    Article  Google Scholar 

  20. Karar ME, Hemdan EE-D, Shouman MA (2021) Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans. Complex Intell Syst 7(1):235–247

    Article  Google Scholar 

  21. Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131

    Article  Google Scholar 

  22. Khalid EA, Chawki Y, Idri A (2021) Automated methods for detection and classification of pneumonia based on x-ray images using deep learning. In Artificial intelligence and blockchain for future cybersecurity applications. Springer International Publishing, Cham, pp 257–284

  23. Khan W, Zaki N, Ali L (2021) Intelligent pneumonia identification from chest x-rays: a systematic literature review. IEEE Access

  24. Khan MA, Kadry S, Zhang Y-D, Akram T, Sharif M, Rehman A, Saba T (2021) Prediction of COVID-19-pneumonia based on selected deep features and one class kernel extreme learning machine. Comput Electr Eng 90:106960

    Article  Google Scholar 

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

  26. Levinsky Y, Mimouni FB, Fisher D, Ehrlichman M (2013) Chest radiography of acute pediatric lower respiratory infections: experience versus an interobserver variation. Acta Paediatr 102(7):e310–e314

    Article  Google Scholar 

  27. Liang G, Zheng L (2020) A transfer learning method with a deep residual network for pediatric pneumonia diagnosis. Comput Methods Prog Biomed 187:104964

    Article  Google Scholar 

  28. Liz H, Sánchez-Montañés M, Tagarro A, Domínguez-Rodríguez S, Dagan R, Camacho D (2021) Ensembles of convolutional neural network models for pediatric pneumonia diagnosis. Futur Gener Comput Syst 122:220–233

    Article  Google Scholar 

  29. Longjiang, E, Zhao B, Liu H, Zheng C, Song X, Cai Y, Liang H (2020) Image-based Deep Learning in Diagnosing the Etiology of Pneumonia on Pediatric Chest X-rays. Pediatr Pulmonol

  30. Masud M, Bairagi AK, Nahid A-A, Sikder N, Rubaiee S, Ahmed A, Anand D (2021) A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm. J Healthcare Eng 2021

  31. Narayanan BN, Davuluru VSP, Hardie RC (2020) Two-stage deep learning architecture for pneumonia detection and its diagnosis in chest radiographs. Med Imaging 2020: Imaging Inf Healthcare, Research, Appl 11318:113180G. International Society for Optics and Photonics

  32. Neuman MI, Graham D, Bachur R (2011) Variation in the use of chest radiography for pneumonia in pediatric emergency departments. Pediatr Emerg Care 27(7):606–610

    Article  Google Scholar 

  33. Nijman, RG, Vergouwe Y, Thompson M, van Veen M, van Meurs AHJ, van der Lei J, Steyerberg EW, Moll HA, Oostenbrink R (2013) Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. Bmj 346

  34. O’Grady K-AF, Torzillo PJ, Frawley K, Chang AB (2014) The radiological diagnosis of pneumonia in children. Pneumonia 5:38–51

    Article  Google Scholar 

  35. O'Grady K-AF, Torzillo PJ, Ruben AR, Taylor-Thomson D, Valery PC, Chang AB (2012) Identification of radiological alveolar pneumonia in children with high rates of hospitalized respiratory infections: comparison of WHO-defined and pediatric pulmonologist diagnosis in the clinical context. Pediatr Pulmonol 47(4):386–392

    Article  Google Scholar 

  36. Omar H, Babalık A (2019) Detection of pneumonia from X-ray images using convolutional neural network. Proceedings Book, p 183

  37. Prina E, Ranzani OT, Torres A (2015) Community-acquired pneumonia. Lancet 386(9998):1097–1108

    Article  Google Scholar 

  38. Rahman T, Chowdhury MEH, Khandakar A, Islam KR, Islam KF, Mahbub ZB, Kadir MA, Kashem S (2020) Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl Sci 10(9):3233

    Article  Google Scholar 

  39. Rajaraman S, Candemir S, Thoma G, Antani S (2019) Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs. Med Imaging 2019: Comput-Aid Diag 10950:109500S. International Society for Optics and Photonics

  40. Rajpal S, Lakhyani N, Singh AK, Kohli R, Kumar N (2021) Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images. Chaos, Solitons Fractals 145:110749

    Article  Google Scholar 

  41. Richardson M, Lakhanpaul M (2007) Assessment and initial management of feverish illness in children younger than five years: summary of NICE guidance. BMJ 334(7604):1163–1164

    Article  Google Scholar 

  42. Siddiqi R (2020) Efficient pediatric pneumonia diagnosis using Depthwise separable convolutions. SN Comput Sci 1(6):343

    Article  Google Scholar 

  43. Sigaúque B, Roca A, Bassat Q, Morais L, Quintó L, Berenguera A, Machevo S et al (2009) Severe pneumonia in Mozambican young children: clinical and radiological characteristics and risk factors. J Trop Pediatr 55(6):379–387

    Article  Google Scholar 

  44. Stephen, O, Sain M, Maduh UJ, Jeong D-U (2019) An efficient deep learning approach to pneumonia classification in healthcare. J Healthcare Eng 2019

  45. Swingler GH (2001) Observer variation in chest radiography of acute lower respiratory infections in children: a systematic review. BMC Med Imaging 1(1):1–5

    Article  Google Scholar 

  46. Test M, Shah SS, Monuteaux M, Ambroggio L, Lee EY, Markowitz RI, Bixby S et al (2013) impact of clinical history on chest radiograph interpretation. J Hosp Med 8(7):359–364

    Article  Google Scholar 

  47. Thakur S, Goplani Y, Arora S, Upadhyay R, Sharma G (2021) Chest X-Ray Images Based Automated Detection of Pneumonia Using Transfer Learning and CNN. In: In Proceedings of International Conference on Artificial Intelligence and Applications, Springer, Singapore, pp 329–335

  48. UNICEF (2016) Unicef data: Monitoring the situation of children and women. Unicef, New York

  49. WHO, World health organization, Pneumonia (2020) https://www.who.int/news-room/fact-sheets/detail/pneumonia. Accessed 23 Jan 2021

  50. Williams GJ, Macaskill P, Kerr M, Fitzgerald DA, Isaacs D, Codarini M, McCaskill M, Prelog K, Craig JC (2013) Variability and accuracy in interpretation of consolidation on chest radiography for diagnosing pneumonia in children under five years of age. Pediatr Pulmonol 48(12):1195–1200

    Article  Google Scholar 

  51. Wingerter SL, Bachur RG, Monuteaux MC, Neuman MI (2012) Application of the world health organization criteria to predict radiographic pneumonia in a US-based pediatric emergency department. Pediatr Infect Dis J 31(6):561–564

    Article  Google Scholar 

  52. Wu X, Hui H, Niu M, Liang L, Wang L, He B, Yang X et al (2020) Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. European J Radiol 128:109041

    Article  Google Scholar 

  53. Wu H, Xie P, Zhang H, Li D, Ming-Cheng (2020) Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. J Intell Fuzzy Syst Preprint

  54. Xavier-Souza G, Vilas-boas AL, Fontoura M-SH, Araújo-Neto CA, Andrade SCS, Cardoso M-RA, Nascimento-Carvalho CM, PNEUMOPAC-efficacy study group (2013) The inter-observer variation of chest radiograph reading in acute lower respiratory tract infection among children. Pediatr Pulmonol 48(5):464–469

    Article  Google Scholar 

  55. Yee SLK, Raymond WJK (2020) Pneumonia diagnosis using chest x-ray images and machine learning. In: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology, pp 101–105

  56. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 15(11):e1002683

    Article  Google Scholar 

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  1. SASTRA Deemed University: Shanmugha Arts Science Technology and Research Academy, Thanjavur, TN, India

    N. Sasikaladevi & A. Revathi

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Sasikaladevi, N., Revathi, A. Intelligent prognostic system for pediatric pneumonia based on sustainable IoHT. Multimed Tools Appl 82, 26901–26917 (2023). https://doi.org/10.1007/s11042-023-14930-z

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