ARCHIVES
Research Article
Comparative Analysis for Prediction of Pneumonia using Deep learning Methods
MananPruthi1
AshishKatyal2
Sanyam3
Rishabh Semwal4
Vijay Kumar5
12345 Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi-110063,India.
Published Online: September-October 2023
Pages: 18-29
Cite this article
No DOIReferences
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6. A.S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN features off-the-shelf: an astounding baseline for recognition, In
Proceedings of the IEEE conference on computer vision and pattern
7. Fafi, Kusworo and Catur, Breast Tumor Detection using Haar-Like Feature Methodon Ultrasono graphy( USG) Imaging,
International Journal of Innovative Research in Advanced Engineering, Volume V,154-
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8. Abiyev R. H., Ma‘aitah M. K. S., Deep Convolution Neural Networks for Chest Diseases Detection, Journal of Health care
Engineering, 2018.
9. A. Krizhevsky, I. Sutskever, & G.E. Hinton, Image Net Classification with Deep Convolution Neural Networks, Neural Information
Processing Systems, 25.10.1145/3065386,2012.
10. M.Cicero, A.Bilbily, E.Colak,T.Dowdell,B.Gra, K.Perapaladas, and
11. J.Barfett, Training and Validating a Deep Convolution Neutral Network for Computer-Aided Detection and Classification of
Abnormalitites on Frontal Chest Radiographs, Investigative Radiology 52(5)281-287,2017.
12. Le, W.T.; Maleki, F.; Romero, F.P.; Forghani, R.; Kadoury, S. Overview of machine learning: Part2. Neuroimaging C
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13. 431.[Cross Ref][PubMed]
14. Ronne berger, O.; Fischer, P.; Brox, T. U-net: Convolution networks for biomedical image segmentation. In Medical Image
Computing and Computer-Assisted Intervention—MICCAI 2015; Springer International Publishing: Cham,
Switzerland,2015;pp.234–241.
15. Milletari,F.; Navab, N.;Ahmadi,S.A.V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation .In
Proceedings of the 2016 Fourth International Conference on 3D Vision(3DV), Stanford ,CA, USA, 25–28October2016;pp.565–571.
16. Jeelani,H.;Martin,J.;Vasquez,F.;Salerno, M.;Weller, D.Image quality affects deep learning reconstruction of MRI. In Proceedings
of the 2018 IEEE 15th International Symposium on Biomedical Imaging(ISBI2018),Washington,DC,USA,4–7 April2018;pp.357–
360.
17. Chlemper,J.S.;Caballero,J.;Hajnal,J.;Price,A.N.;Rueckert,D.Adeep cascade of convolution neural networks for dynamic MR image
reconstruction.IEEETrans.Med.Imaging2017,37,491–503.[CrossRef][PubMed]
18. Rajpurkar, P.; Irvin, J.; Zhu, K.; Yang, B.; Mehta,H.; Duan, T.; Ding,D.; Bagul, A.; Langlotz, C.; Shpanskaya, K.; et al. Chexnet:
Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv2017,arXiv:1711.05225.
19. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. DenselyConnected Convolutional Networks. In Proceedings of the 2017
IEEE Conference on Computer Vis ion and Pattern Recognition(CVPR),Honolulu, HI, USA,21–26 July2017;pp.2261–2269.
20. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for
imagerecognition.InProceedingsoftheIEEEConferenceonComputerVisionandPatternRecognition,Las Vegas,NV,USA,27–
30June2016;pp.770–778.
21. Yu,F.;Koltun,V.;Funkhouser,T.DilatedResidualNetworks.InProceedings of the 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, USA, 25–30 June 2017; pp.636–644.
22. Liang, G.; Zheng, L.A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Com put. Methods
ProgramsBiomed.2020,187,104964.[Cross Ref][Pub Med]
23. Jain,R.;Nagrath,P.;Kataria,G.;Kaushik,V.S.;Hemanth,D.J.Pneumonia detection in chest X-ray images using convolution
neuralnetworksandtransferlearning.Measurment2020,165.[Cros sRef]
24. Verma, D.; Bose, C.; Tufchi, N.; Pant, K.; Tripathi, V.; Thapliyal, A. An efficient framework for identification of Tuberculosis and
Pneumonia inchest X-ray images using Neural Network. ProcediaComput. Sci. 2020,171,217–224.[Cross Ref]
25. Ayan,E.;Unver,H.M.Diagnos is of Pneumonia from Chest X-RayImages Using Deep Learning. In Proceedings of the 2019 ScientificMeeting on Electrical- Electronics& Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 24–26 April 2019;
pp. 1–5.
26. Elshennawy, N.M.; Ibrahim, D.M. Deep-pneumonia framework using deep learning models based onchest X-ray images.
Diagnostics 2020,10,649.[Cross Ref][PubMed].
27. Li, B.; Kang, G.; Cheng, K.; Zhang, N. Attention-Guided Convolution Neural Network for Detecting Pneumonia on Chest X-Rays.
In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(EMBC), Berlin, Germany, 23–27July2019;pp.4851–4854.
28. Guo, X.; Yuan, Y. Triple ANet: Adaptive Abnormal-aware Attention Network forWCEImage Classification. In Proceedings of the
19th International Conference on Application of Concurrency to System Design (ACSD 2019), Aachen, Germany, 23–28 June 2019;
pp. 293–301.
29. Baltruschat,I.M.; Nickisch, H.; Grass, M.; Knopp, T.; Saalbach, A.Comparison of Deep Learning Approaches for Multi-Label Chest
X- Ray Classification. Sci.Rep.2019,9,1–10.[Cross Ref][Pub Med]
30. Nahid,A.-A.;Sikder,N.;Bairagi,A.K.;Razzaque,A.;Masud,M.;Kouzani,A.Z.;Mahmud,M.A.P.ANovel Method to Identify Pneumonia
through Analyzing Chest Radio graphs Employing Multichannel Convolution Neural Network. Sensors 2020, 20,
3482.[CrossRef][PubMed]
31. Odaibo, D.; Zhang, Z.; Skidmore, F.; Tanik, M. Detection of visual signals for
pneumoniainchestradiographsusingweaksupervision.2019 SoutheastCon2019,1–5.[Cross Ref].
32. D. Kermany, K.Zhang,M.Goldbaum,― Labeled Optical Coherence Tomography(OCT)and Chest X-Ray Images for Classification‖,
Mendeley Data,v2,2018.AccessedJuly 2020.[Online].Available: http://dx.doi.org/10.17632/rscbjbr9sj.2
33. R.C.González,R.E.Woods.― DigitalImage Processing‖, PrenticeHall, 2007,pp85.[32].A.Geron,―Hands-On Machine Learning with
Scikit- Learn, Keras,and TensorFlow‖,O‘Reilly Media,Inc.,Canada,2019.
34. Jupyter Notebook:Project Jupyter.AccessedJuly2020.[Online].
35. Available: http://jupyter.org
36. D.Varshni,K.Thakral,L.Agarwal,R.Nijhawan,A.Mittal,"PneumoniaDetectionUsingCNNbasedFeatureExtraction,"2019IEEEInternati
onalConf.onElectrical,ComputerandCommunication
37. Technologies(ICECCT),Coimbatore,India,2019,pp1-7.[35].V.SirishKaushiketal.,―PneumoniaDetectionUsingConvolutional Neural
Networks (CNNs)‖,In: Singh P., Pawłowski W.,Tanwar S.,KumarN.,RodriguesJ.,ObaidatM.(eds)ProceedingsofFirst
InternationalConferenceonComputing,Communications,andCyberSecurity (IC4S 2019). Lecture Notes inNetworks and
Systems,vol121.Springer,Singapore.
38. Dataset-Link:https://www.kaggle.com/datasets/paultimothymooney/chest-xray- pneumonia
39. https://towardsdatascience.com/deep-learning-for-detecting-pneumonia- from-x-ray-images-fc9a3d9fdba8
40. https://towardsdatascience.com/step-by-step-vgg16-implementation-in- keras-for-beginners-a833c686ae6c
41. https://upload.wikimedia.org/wikipedia/commons/7/76/Random_forest_ diagram_complete.png
2. E.Sayed,et. al, Computer- aided Diagnosis of Human Brain Tumor though MRI:A Survey and an ewalgorithm, Expert System with
Application s(41):2014.
3. D.K. Das, M. Ghosh, M. Pal, A.K. Maiti, and C. Chakraborty, Machine learning approach for auto malteds careening of malaria
parasite using light micros copicimages, Micron45,97106,2013.
4. M. Poostchi, K. Silamut, R. Maude, S. Jaeger, and G. Thoma, Imageanalys is and machine learning for detecting malaria,
Translational Research, 2018.
5. N.E.Ross, C.J.Pritchard ,D.M.Rubin, and A.G.Duse, Auto mated image processing method for the diagnosis and classification of
malar aunt in bloods mears, Medical and Biological Engineering and Computing 44,5,427436,2006.
6. A.S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN features off-the-shelf: an astounding baseline for recognition, In
Proceedings of the IEEE conference on computer vision and pattern
7. Fafi, Kusworo and Catur, Breast Tumor Detection using Haar-Like Feature Methodon Ultrasono graphy( USG) Imaging,
International Journal of Innovative Research in Advanced Engineering, Volume V,154-
157.doi://10.26562/IJIRAE.2018.MYAE10082.
8. Abiyev R. H., Ma‘aitah M. K. S., Deep Convolution Neural Networks for Chest Diseases Detection, Journal of Health care
Engineering, 2018.
9. A. Krizhevsky, I. Sutskever, & G.E. Hinton, Image Net Classification with Deep Convolution Neural Networks, Neural Information
Processing Systems, 25.10.1145/3065386,2012.
10. M.Cicero, A.Bilbily, E.Colak,T.Dowdell,B.Gra, K.Perapaladas, and
11. J.Barfett, Training and Validating a Deep Convolution Neutral Network for Computer-Aided Detection and Classification of
Abnormalitites on Frontal Chest Radiographs, Investigative Radiology 52(5)281-287,2017.
12. Le, W.T.; Maleki, F.; Romero, F.P.; Forghani, R.; Kadoury, S. Overview of machine learning: Part2. Neuroimaging C
lin.N.Am.2020,30,417–
13. 431.[Cross Ref][PubMed]
14. Ronne berger, O.; Fischer, P.; Brox, T. U-net: Convolution networks for biomedical image segmentation. In Medical Image
Computing and Computer-Assisted Intervention—MICCAI 2015; Springer International Publishing: Cham,
Switzerland,2015;pp.234–241.
15. Milletari,F.; Navab, N.;Ahmadi,S.A.V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation .In
Proceedings of the 2016 Fourth International Conference on 3D Vision(3DV), Stanford ,CA, USA, 25–28October2016;pp.565–571.
16. Jeelani,H.;Martin,J.;Vasquez,F.;Salerno, M.;Weller, D.Image quality affects deep learning reconstruction of MRI. In Proceedings
of the 2018 IEEE 15th International Symposium on Biomedical Imaging(ISBI2018),Washington,DC,USA,4–7 April2018;pp.357–
360.
17. Chlemper,J.S.;Caballero,J.;Hajnal,J.;Price,A.N.;Rueckert,D.Adeep cascade of convolution neural networks for dynamic MR image
reconstruction.IEEETrans.Med.Imaging2017,37,491–503.[CrossRef][PubMed]
18. Rajpurkar, P.; Irvin, J.; Zhu, K.; Yang, B.; Mehta,H.; Duan, T.; Ding,D.; Bagul, A.; Langlotz, C.; Shpanskaya, K.; et al. Chexnet:
Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv2017,arXiv:1711.05225.
19. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. DenselyConnected Convolutional Networks. In Proceedings of the 2017
IEEE Conference on Computer Vis ion and Pattern Recognition(CVPR),Honolulu, HI, USA,21–26 July2017;pp.2261–2269.
20. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for
imagerecognition.InProceedingsoftheIEEEConferenceonComputerVisionandPatternRecognition,Las Vegas,NV,USA,27–
30June2016;pp.770–778.
21. Yu,F.;Koltun,V.;Funkhouser,T.DilatedResidualNetworks.InProceedings of the 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, USA, 25–30 June 2017; pp.636–644.
22. Liang, G.; Zheng, L.A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Com put. Methods
ProgramsBiomed.2020,187,104964.[Cross Ref][Pub Med]
23. Jain,R.;Nagrath,P.;Kataria,G.;Kaushik,V.S.;Hemanth,D.J.Pneumonia detection in chest X-ray images using convolution
neuralnetworksandtransferlearning.Measurment2020,165.[Cros sRef]
24. Verma, D.; Bose, C.; Tufchi, N.; Pant, K.; Tripathi, V.; Thapliyal, A. An efficient framework for identification of Tuberculosis and
Pneumonia inchest X-ray images using Neural Network. ProcediaComput. Sci. 2020,171,217–224.[Cross Ref]
25. Ayan,E.;Unver,H.M.Diagnos is of Pneumonia from Chest X-RayImages Using Deep Learning. In Proceedings of the 2019 ScientificMeeting on Electrical- Electronics& Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 24–26 April 2019;
pp. 1–5.
26. Elshennawy, N.M.; Ibrahim, D.M. Deep-pneumonia framework using deep learning models based onchest X-ray images.
Diagnostics 2020,10,649.[Cross Ref][PubMed].
27. Li, B.; Kang, G.; Cheng, K.; Zhang, N. Attention-Guided Convolution Neural Network for Detecting Pneumonia on Chest X-Rays.
In Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(EMBC), Berlin, Germany, 23–27July2019;pp.4851–4854.
28. Guo, X.; Yuan, Y. Triple ANet: Adaptive Abnormal-aware Attention Network forWCEImage Classification. In Proceedings of the
19th International Conference on Application of Concurrency to System Design (ACSD 2019), Aachen, Germany, 23–28 June 2019;
pp. 293–301.
29. Baltruschat,I.M.; Nickisch, H.; Grass, M.; Knopp, T.; Saalbach, A.Comparison of Deep Learning Approaches for Multi-Label Chest
X- Ray Classification. Sci.Rep.2019,9,1–10.[Cross Ref][Pub Med]
30. Nahid,A.-A.;Sikder,N.;Bairagi,A.K.;Razzaque,A.;Masud,M.;Kouzani,A.Z.;Mahmud,M.A.P.ANovel Method to Identify Pneumonia
through Analyzing Chest Radio graphs Employing Multichannel Convolution Neural Network. Sensors 2020, 20,
3482.[CrossRef][PubMed]
31. Odaibo, D.; Zhang, Z.; Skidmore, F.; Tanik, M. Detection of visual signals for
pneumoniainchestradiographsusingweaksupervision.2019 SoutheastCon2019,1–5.[Cross Ref].
32. D. Kermany, K.Zhang,M.Goldbaum,― Labeled Optical Coherence Tomography(OCT)and Chest X-Ray Images for Classification‖,
Mendeley Data,v2,2018.AccessedJuly 2020.[Online].Available: http://dx.doi.org/10.17632/rscbjbr9sj.2
33. R.C.González,R.E.Woods.― DigitalImage Processing‖, PrenticeHall, 2007,pp85.[32].A.Geron,―Hands-On Machine Learning with
Scikit- Learn, Keras,and TensorFlow‖,O‘Reilly Media,Inc.,Canada,2019.
34. Jupyter Notebook:Project Jupyter.AccessedJuly2020.[Online].
35. Available: http://jupyter.org
36. D.Varshni,K.Thakral,L.Agarwal,R.Nijhawan,A.Mittal,"PneumoniaDetectionUsingCNNbasedFeatureExtraction,"2019IEEEInternati
onalConf.onElectrical,ComputerandCommunication
37. Technologies(ICECCT),Coimbatore,India,2019,pp1-7.[35].V.SirishKaushiketal.,―PneumoniaDetectionUsingConvolutional Neural
Networks (CNNs)‖,In: Singh P., Pawłowski W.,Tanwar S.,KumarN.,RodriguesJ.,ObaidatM.(eds)ProceedingsofFirst
InternationalConferenceonComputing,Communications,andCyberSecurity (IC4S 2019). Lecture Notes inNetworks and
Systems,vol121.Springer,Singapore.
38. Dataset-Link:https://www.kaggle.com/datasets/paultimothymooney/chest-xray- pneumonia
39. https://towardsdatascience.com/deep-learning-for-detecting-pneumonia- from-x-ray-images-fc9a3d9fdba8
40. https://towardsdatascience.com/step-by-step-vgg16-implementation-in- keras-for-beginners-a833c686ae6c
41. https://upload.wikimedia.org/wikipedia/commons/7/76/Random_forest_ diagram_complete.png
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