Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models

doi:10.23940/ijpe.23.03.p3.175183

Abstract

Abstract: A range of infectious bacteria and non-infectious factors can both induce and lead to pneumonia, which is an illness of the pulmonary parenchyma. Several age categories are vulnerable, but children under the age of five tend to be especially vulnerable. Chest x-rays, the most frequent radiological test, are a highly significant modality for which numerous uses have been studied. Additionally, x-ray equipment has an upside due to their reduced radiation exposure over imaging technologies like tomography and their potential for accessibility from remote locations. Unfortunately, it might be challenging for physicians to identify pediatric pneumonia because x-ray scans are not always clear or because of human traits like weariness and inattentiveness. In this research, the authors provide a framework for brightness preserving and contrast boosting to strengthen the precision of existing deep learning models. Different cutting-edge deep learning models including VGG16, GoogLeNet, AlexNet, DenseNet-121, InceptionV3, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet are tested both with and without the improvement suggested in this research. The results obtained clearly show the improvement in classification accuracy attained after implementing the proposed image enhancement. The experiments exhibit a maximum of 10.57% improvement in the overall accuracy attained without enhancing the image with the proposed framework. The highest accuracy recorded is 91.05% with RestNet-101.

Key words: chest x-ray, classification, deep learning, image enhancement, pediatric pneumonia

Vaishali Arya and Tapas Kumar. Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models [J]. Int J Performability Eng, 2023, 19(3): 175-183.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Ayan E., Karabulut B., andÜnver H.M.Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images. Arabian Journal for Science and Engineering, pp. 1-17, 2022.
2. McAllister D.A., Liu L., Shi T., Chu Y., Reed C., Burrows J., Adeloye D., Rudan I., Black R.E., Campbell H., andNair, H. Global Regional, and National Estimates of Pneumonia Morbidity and Mortality in Children Younger Than 5 Years Between2000 and 2015: A Systematic Analysis. The Lancet Global Health, vol. 7, no. 1, pp. e47-e57, 2019.
3. Drake D.E., Cohen A., andCohn J.National Hospital Antibiotic Timing Measures for Pneumonia and Antibiotic Overuse. Quality Management in Healthcare, vol. 16, no. 2, pp. 113-122, 2007.
4. World Health Organization.Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children, no. WHO/V&B/01.35, 2001.
5. Ker J., Wang L., Rao J., andLim T.Deep Learning Applications in Medical Image Analysis. IEEE Access, no. 6, pp. 9375-9389, 2017.
6. Neuman M.I., Lee E.Y., Bixby S., Diperna S., Hellinger J., Markowitz R., Servaes S., Monuteaux M.C., andShah S.S.Variability in the Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children. Journal of hospital medicine, vol. 7, no. 4, pp. 294-298, 2012.
7. Loey M., Smarandache F., andM. Khalifa, N.E. Within the Lack of Chest COVID-19 X-Ray Dataset: A Novel Detection Model based on GAN and Deep Transfer Learning. Symmetry, vol. 12, no. 4, pp. 651, 2020.
8. Batra S., Sharma H., Boulila W., Arya V., Srivastava P., Khan M.Z., andKrichen M.An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-Ray Scans. Sensors, vol. 22, no. 19, pp. 7474, 2022.
9. Shen D., Wu G., andSuk H.I.Deep Learning in Medical Image Analysis. Annual review of biomedical engineering, vol. 19, pp. 221-248, 2017.
10. Grewal M., Srivastava M.M., Kumar P., andVaradarajan, S. Radnet: Radiologist Level Accuracy using Deep Learning for Hemorrhage Detection in CT Scans. In2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE, pp. 281-284, 2018.
11. Mazurowski M.A., Buda M., Saha A., andBashir M.R.Deep Learning in Radiology: An Overview of the Concepts and a Survey of the State of the Art with Focus on MRI. Journal of magnetic resonance imaging, vol. 49, no. 4, pp. 939-954, 2019.
12. Batra S., Khurana R., Khan M.Z., Boulila W., Koubaa A., andSrivastava P.A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records. Entropy, vol. 24, no. 4, pp. 533, 2022.
13. Tai S.C., Liao T.W., Chang Y.Y., andYeh, C.P. Automatic White Balance Algorithm through the Average Equalization and Threshold. In2012 8th International Conference on Information Science and Digital Content Technology (ICIDT2012), IEEE, vol. 3, pp. 571-576, 2012.
14. Huo J.Y., Chang Y.L., Wang J., andWei X.X.Robust Automatic White Balance Algorithm using Gray Color Points in Images. IEEE Transactions on Consumer Electronics, vol. 52, no. 2, pp. 541-546, 2006.
15. Kim S., Kim W.J., andKim, S.D. Automatic White Balance based on Adaptive Feature Selection with Standard Illuminants. In2008 15th IEEE International Conference on Image Processing, IEEE, pp. 485-488, 2008.
16. Lam, E.Y. Combining Gray World and Retinex Theory for Automatic White Balance in Digital Photography. In Proceedings of the Ninth International Symposium on Consumer Electronics (ISCE2005), IEEE, pp. 134-139, 2005.
17. Bae Y., Jang J.H., andRa, J.B. Gamut-Adaptive Correction in Color Image Processing. In2010 IEEE International Conference on Image Processing, IEEE, pp. 3597-3600, 2010.
18. Chen, C.L. and Lin, S.H.Intelligent Color Temperature Estimation using Fuzzy Neural Network with Application to Automatic White Balance. Expert Systems with Applications, vol. 38, no. 6, pp. 7718-7728, 2011.
19. Sanila K.H., Balakrishnan A.A., andSupriya, M.H. Underwater Image Enhancement using White Balance, USM and CLHE. In2019 International Symposium on Ocean Technology (SYMPOL), IEEE, pp. 106-116, 2019.
20. Albahli S., Rauf H.T., Algosaibi A., andBalas V.E.AI-Driven Deep CNN Approach for Multi-Label Pathology Classification using Chest X-Rays. PeerJ Computer Science, vol.7, p.e495, 2021.
21. Chandra, T.B. and Verma, K. Pneumonia Detection on Chest X-Ray using Machine Learning Paradigm. In Proceedings of 3rd International Conference on Computer Vision and Image Processing: CVIP2018, Springer Singapore, vol. 1, pp. 21-33, 2020.
22. Kuo K.M., Talley P.C., Huang C.H., andCheng L.C.Predicting Hospital-Acquired Pneumonia Among Schizophrenic Patients: A Machine Learning Approach. BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp.1-8, 2019.
23. Yue H., Yu Q., Liu C., Huang Y., Jiang Z., Shao C., Zhang H., Ma B., Wang Y., Xie G., andZhang H.Machine Learning-Based CT Radiomics Method for Predicting Hospital Stay in Patients with Pneumonia Associated with SARS-CoV-2 Infection: A Multicenter Study. Annals of translational medicine, vol. 8, no. 14, 2020.
24. Kadry S., Nam Y., Rauf H.T., Rajinikanth V., andLawal, I.A. Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study. In2021 seventh international conference on bio signals, images, and instrumentation (ICBSII), IEEE, pp. 1-5, 2021.
25. Sharma H., Jain J.S., Bansal P., andGupta S. Feature Extraction and Classification of Chest X-Ray Images using CNN to Detect Pneumonia. In2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, pp. 227-231, 2020.
26. Stephen O., Sain M., Maduh U.J., andJeong D.U.An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. Journal of healthcare engineering, 2019.
27. Kermany D., Zhang K., andGoldbaum M.Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. Mendeley data, vol. 2, no. 2, pp. 651, 2018.
28. Rajpurkar P., Irvin J., Zhu K., Yang B., Mehta H., Duan T., Ding D., Bagul A., Langlotz C., Shpanskaya K., andLungren, M.P. Chexnet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint arXiv:1711.05225, 2017.
29. Janizek J.D., Erion G., DeGrave, A.J., and Lee, S.I. An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs. In Proceedings of the ACM conference on health, inference, and learning, pp. 69-79, 2020.
30. Zhang J., Xie Y., Pang G., Liao Z., Verjans J., Li W., Sun Z., He J., Li Y., Shen C., andXia Y.Viral Pneumonia Screening on Chest X-Rays using Confidence-Aware Anomaly Detection. IEEE transactions on medical imaging, vol. 40, no. 3, pp. 879-890, 2020.
31. Tuncer T., Ozyurt F., Dogan S., andSubasi A.A Novel Covid-19 and Pneumonia Classification Method based on F-Transform. Chemometrics and intelligent laboratory systems, vol. 210, pp. 104256, 2021.
32. Rahman T., Chowdhury M.E., Khandakar A., Islam K.R., Islam K.F., Mahbub Z.B., Kadir M.A., andKashem S.Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-Ray. Applied Sciences, vol. 10, no. 9, pp. 3233, 2020.
33. Liang, G. and Zheng, L.A Transfer Learning Method with Deep Residual Network for Pediatric Pneumonia Diagnosis. Computer methods and programs in biomedicine, vol. 187, pp. 104964, 2020.
34. Ibrahim A.U., Ozsoz M., Serte S., Al-Turjman, F., and Yakoi, P.S. Pneumonia Classification using Deep Learning from Chest X-Ray Images During COVID-19. Cognitive Computation, pp. 1-13, 2021.
35. Deng J., Dong W., Socher R., Li L.J., Li K., andFei-Fei, L. Imagenet: A Large-Scale Hierarchical Image Database. In2009 IEEE conference on computer vision and pattern recognition, IEEE, pp. 248-255, 2009.
36. White Balance, https://docs.gimp.org/2.10/en/gimp-layer-white-balance.html, accessed on November 18, 2022.
37. Reza A.M.Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement. Journal of VLSI signal processing systems for signal, image and video technology, vol. 38, pp. 35-44, 2004.
38. Pizer S.M., Amburn E.P., Austin J.D., Cromartie R., Geselowitz A., Greer T., ter Haar Romeny, B., Zimmerman, J.B., and Zuiderveld, K. Adaptive Histogram Equalization and Its Variations. Computer vision, graphics, and image processing, vol. 39, no. 3, pp. 355-368, 1987.
39. Pizer S.M.Contrast-Limited Adaptive Histogram Equalization: Speed and Effectiveness Stephen M. Pizer, R. Eugene Johnston, James P. Ericksen, Bonnie C. Yankaskas, Keith E. Muller Medical Image Display Research Group. In Proceedings of the first conference on visualization in biomedical computing, Atlanta, Georgia, vol. 337, pp. 1, 1990.
40. Wang, J. and Perez, L.The Effectiveness of Data Augmentation in Image Classification using Deep Learning. Convolutional Neural Networks Vis. Recognit, vol. 11, no. 2017, pp. 1-8, 2017.
41. Nijaguna G.S., Babu J.A., Parameshachari B.D., de Prado, R.P., and Frnda, J. Quantum Fruit Fly Algorithm and ResNet50-VGG16 for Medical Diagnosis. Applied Soft Computing, vol. 136, pp. 110055, 2023.
42. Li J., Ke L., Du Q., Ding X., andChen X.Research on the Classification of ECG and PCG Signals based on BiLSTM-GoogLeNet-DS. Applied Sciences, vol. 12, no. 22, pp. 11762, 2022.
43. Alsharabi N., Shahwar T., Rehman A.U., andAlharbi Y.Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet-Quantum Learning. Mathematics, vol. 11, no. 2, pp. 376, 2023.
44. Mao W.L., Chen W.C., Wang C.T., andLin Y.H.Recycling Waste Classification using Optimized Convolutional Neural Network. Resources, Conservation and Recycling, vol. 164, pp. 105132, 2021.
45. Kumar S., Gupta S.K., Kaur M., andGupta U.VI-NET: A Hybrid Deep Convolutional Neural Network using VGG and Inception V3 Model for Copy-Move Forgery Classification. Journal of Visual Communication and Image Representation, vol. 89, pp. 103644, 2022.
46. Sun T., Ding S., andGuo L.Low-Degree Term First in ResNet, Its Variants and the Whole Neural Network Family. Neural Networks, vol. 148, pp. 155-165, 2022.
47. MS, M. and SS, S.R.Optimal Squeeze Net with Deep Neural Network-Based Arial Image Classification Model in Unmanned Aerial Vehicles. Traitement du Signal, vol. 39, no. 1, 2022.
48. Hidayatullah, R.C. and Violina, S.Convolutional Neural Network Architecture and Data Augmentation for Pneumonia Classification from Chest X-Rays Images. Int J Innov Sci Res Technol, vol. 5, pp. 158-164, 2020.

[1]	Pancham Singh, Updesh Kumar Jaiswal, Eshank Jain, Nikhil Kumar, and Vimlesh Mishra. A Novel Methodology Utilizing Modern CCTV Cameras and Software as a Service Model for Crime Detection and Prediction [J]. Int J Performability Eng, 2025, 21(2): 112-121.
[2]	Seema Kalonia and Amrita Upadhyay. Comparative Analysis of Machine Learning Model and PSO Optimized CNN-RNN for Software Fault Prediction [J]. Int J Performability Eng, 2025, 21(1): 48-55.
[3]	Manu Banga. Enhancing Software Fault Prediction using Machine Learning [J]. Int J Performability Eng, 2024, 20(9): 529-540.
[4]	Aditya Dayal Tyagi, and Krishna Asawa. Influence Maximization in Social Network using Community Detection and Node Modularity [J]. Int J Performability Eng, 2024, 20(9): 552-562.
[5]	Ekta Singh, and Parma Nand. Efficient Multi-Class Facial Emotion Recognition using YOLOv9: A Deep Learning Approach for Real-Time Applications [J]. Int J Performability Eng, 2024, 20(9): 581-590.
[6]	Manpreet Singh, Gauri Jindal, Akshita Oberoi, and Rohan Dhangar. Improving Crime Detection Through Geo-MDA: A Hybrid Linear Regression Approach in Data Mining [J]. Int J Performability Eng, 2024, 20(8): 469-477.
[7]	Nilesh Shelke, Deepali Sale, Sagar Shinde, Atul Kathole, and Rachna Somkunwar. A Comprehensive Framework for Facial Emotion Detection using Deep Learning [J]. Int J Performability Eng, 2024, 20(8): 487-497.
[8]	Lakshya Vaswani, Sai Sri Harsha, Subham Jaiswal, and Aju D. Unravelling Complexity: Investigating the Effectiveness of SHAP Algorithm for Improving Explainability in Network Intrusion System Across Machine and Deep Learning Models [J]. Int J Performability Eng, 2024, 20(7): 421-431.
[9]	Rohit Kumar Verma and Sukhvir Singh. A Hybrid Framework of Resource Allocation using Firefly and Deep Learning in Big Data Scheduling [J]. Int J Performability Eng, 2024, 20(6): 333-343.
[10]	Vikas Verma, Arun Malik, and Isha Batra. Analyzing and Classifying Malware Types on Windows Platform using an Ensemble Machine Learning Approach [J]. Int J Performability Eng, 2024, 20(5): 312-318.
[11]	Priya Singh and Rajalakshmi Krishnamurthi. AgriGuard: IoT-Powered Real-Time Object Detection and Alert System for Intelligent Surveillance [J]. Int J Performability Eng, 2024, 20(4): 232-241.
[12]	Ujjwal Deep, Sushant Kumar, and Kanika Singla. Integrating Deep Learning Architectures for Enhanced Human Action Recognition: An Ensemble Approach [J]. Int J Performability Eng, 2024, 20(4): 253-262.
[13]	Manu Jyoti Gupta and Parveen Sehgal. Optimizing Credit Card Fraud Detection: Classifier Performance and Feature Selection Empowered by Grasshopper Algorithm [J]. Int J Performability Eng, 2024, 20(3): 177-185.
[14]	Atul Kumar and Gurpreet Singh Lehal. Layout Detection of Punjabi Newspapers using the YOLOv8 Model [J]. Int J Performability Eng, 2024, 20(3): 186-193.
[15]	Shou-Yu Lee, Yu-Sheng Chu, Tzu-Wei Hsu, I-Hsiang Yu, and W. Eric Wong. Enhanced Recognition Approach for Herb Medicine using YOLOv8 in Medical Information Systems [J]. Int J Performability Eng, 2024, 20(12): 713-722.