Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS
DOI: http://dx.doi.org/10.62527/joiv.8.1.1728
Abstract
Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.
Keywords
Full Text:
PDFReferences
World Health Organization, “No Title,” 2021. https://www.who.int/news-room/fact-sheets/detail/pneumonia
and B. I. National Heart, Lung, “No Title,” 2022. https://www.nhlbi.nih.gov/health/pneumonia
A. K. Acharya and R. Satapathy, “A Deep Learning Based Approach towards the Automatic Diagnosis of Pneumonia from Chest Radio-Graphs,” vol. 13, no. March, pp. 449–455, 2020.
P. Rajpurkar et al., “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” pp. 3–9, 2017, [Online]. Available: http://arxiv.org/abs/1711.05225
A. Pant, A. Jain, K. C. Nayak, D. Gandhi, and B. G. Prasad, “Pneumonia Detection: An Efficient Approach Using Deep Learning,” 2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, 2020, doi: 10.1109/ICCCNT49239.2020.9225543.
S. Shah, H. Mehta, and P. Sonawane, “Pneumonia detection using convolutional neural networks,” Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 933–939, 2020, doi: 10.1109/ICSSIT48917.2020.9214289.
I. Departement, U. I. Lamongan, and I. Technology, “Lung X-Ray Image Enhancement to Identify Pneumonia with CNN 1st N ur N afi’iyah,” pp. 421–426, 2021.
S. A. Khoiriyah, A. Basofi, and A. Fariza, “Convolutional Neural Network for Automatic Pneumonia Detection in Chest Radiography,” IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., pp. 476–480, 2020, doi: 10.1109/IES50839.2020.9231540.
V. Nair, S. Suranglikar, S. Deshmukh, and Y. Gavhane, “Multi-labelled ocular disease diagnosis enforcing transfer learning,” 2021 55th Annu. Conf. Inf. Sci. Syst. CISS 2021, pp. 2–7, 2021, doi: 10.1109/CISS50987.2021.9400227.
I. Bello et al., “Revisiting ResNets: Improved Training and Scaling Strategies,” Adv. Neural Inf. Process. Syst., vol. 27, no. NeurIPS, pp. 22614–22627, 2021.
S. Showkat and S. Qureshi, “Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia,” Chemom. Intell. Lab. Syst., vol. 224, no. March, p. 104534, 2022, doi: 10.1016/j.chemolab.2022.104534.
Kaggle, “No Title.” https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
G. U. Nneji, J. Cai, J. Deng, H. N. Monday, E. C. James, and C. C. Ukwuoma, “Multi-Channel Based Image Processing Scheme for Pneumonia Identification,” Diagnostics, vol. 12, no. 2, pp. 1–26, 2022, doi: 10.3390/diagnostics12020325.
U. Dubey and R. K. Chaurasiya, “Efficient Traffic Sign Recognition Using CLAHE-Based Image Enhancement and ResNet CNN Architectures,” Int. J. Cogn. Informatics Nat. Intell., vol. 15, no. 4, pp. 1–19, 2021, doi: 10.4018/ijcini.295811.
B. Ramasubramanian and S. Selvaperumal, “A comprehensive review on various preprocessing methods in detecting diabetic retinopathy,” Int. Conf. Commun. Signal Process. ICCSP 2016, pp. 642–646, 2016, doi: 10.1109/ICCSP.2016.7754220.
Pavan, A. C., S. Lakshmi, and Somashekara MT, “An Improved Method for Reconstruction and Enhancing Dark Images based on CLAHE,” International Research Journal on Advanced Science Hub, vol. 5, no. 2, pp. 41-46, 2023, doi: http://dx.doi.org/10.47392/irjash.2023.011.
M. Siddhartha and A. Santra, “COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19,” pp. 1–25, 2020, [Online]. Available: http://arxiv.org/abs/2006.13873
Z. Mushtaq, S. F. Su, and Q. V. Tran, “Spectral images based environmental sound classification using CNN with meaningful data augmentation,” Appl. Acoust., vol. 172, p. 107581, 2021, doi: 10.1016/j.apacoust.2020.107581.
P. Naveen and B. Diwan, “Pre-trained VGG-16 with CNN architecture to classify X-Rays images into normal or pneumonia,” 2021 Int. Conf. Emerg. Smart Comput. Informatics, ESCI 2021, pp. 102–105, 2021, doi: 10.1109/ESCI50559.2021.9396997.
H. T. Thanh, P. H. Yen, and T. B. Ngoc, “Pneumonia Classification in X-ray Images Using Artificial Intelligence Technology,” Proc. 2020 Appl. New Technol. Green Build. ATiGB 2020, no. March, pp. 25–30, 2021, doi: 10.1109/ATiGB50996.2021.9423017.
T. Rahman, M. E. H. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam, Z. B. Mahbub, M. A. Kadir, S. Kashem, “Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray, ” Applied Sciences, vol. 10, no. 9, 2020, doi:10.3390/app10093233.
X. Li, F. Chen, H. Hao, and M. Li, “A Pneumonia Detection Method Based on Improved Convolutional Neural Network,” Proc. 2020 IEEE 4th Inf. Technol. Networking, Electron. Autom. Control Conf. ITNEC 2020, no. Itnec, pp. 488–493, 2020, doi: 10.1109/ITNEC48623.2020.9084734.
B. D. Satoto, M. I. Utoyo, R. Rulaningtyas, and E. B. Koendhori, “Custom convolutional neural network with data augmentation to predict Pneumonia COVID19,” IBIOMED 2020 - Proc. 37th Int. Conf. Biomed. Eng., pp. 71–76, 2020, doi: 10.1109/IBIOMED50285.2020.9487567.
M. Aledhari, S. Joji, M. Hefeida, and F. Saeed, “Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs,” Proc. - 2019 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2019, pp. 2405–2412, 2019, doi: 10.1109/BIBM47256.2019.8983114.
H. Sharma, J. S. Jain, P. Bansal, and S. Gupta, “Feature extraction and classification of chest X-ray images using CNN to detect pneumonia,” Proc. Conflu. 2020 - 10th Int. Conf. Cloud Comput. Data Sci. Eng., pp. 227–231, 2020, doi: 10.1109/Confluence47617.2020.9057809.
İstanbul AREL Üniversitesi, IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers. Turkey Section, and Institute of Electrical and Electronics Engineers, “Scientific Meeting on Electrical-Electronics, Computer and Biomedical Engineering : 24-26 April 2019 : Istanbul AREL University, Kemal Gözükara Campus - Prof. Dr. Aziz Sancar Conference Hall = Uluslararası Katılımlı Elektrik-Elektronik, Bilgisayar, Biyo,” pp. 0–4, 2019.
X. Liu, Z. Wang, L. Zheng, and J. Gao, “Pneumonia Recognition Based on Convolutional Neural Network Feature Map Fusion,” J. Phys. Conf. Ser., vol. 1757, no. 1, pp. 0–8, 2021, doi: 10.1088/1742-6596/1757/1/012047.
Z. Rustam, R. P. Yuda, H. Alatas, and C. Aroef, “Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 3, pp. 1522–1528, 2020, doi: 10.12928/TELKOMNIKA.v18i3.14839.
A. H. Ahnafi, A. Arifianto, and K. N. Ramadhani, “Pneumonia Classification from X-ray Images using Residual Neural Network,” Ind. J. Comput., vol. 5, no. September, pp. 43–54, 2020, doi: 10.21108/indojc.2020.5.2.454.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.