NEWS

AUTOMATIC DETECTION OF PNEUMONIA USING CONCATENATED CONVOLUTIONAL NEURAL NETWORK


(Received: 12-Dec.-2022, Revised: 8-Mar.-2023 and 14-Mar.-2023 , Accepted: 30-Mar.-2023)
Pneumonia is a life-threatening disease and early detection can save lives. Many automated systems have contributed to the detection of this disease and currently, deep-learning models have become among the most widely used models for building these systems. In this study, two deep-learning models are combined: DenseNet169 and pre-activation ResNet models and used for automatic detection of pneumonia. Two methods are used to deal with the problem of unbalanced data: class weight, which enables to control the percentage of data to be used from the original data for each class of data, while the other method is resampling, in which modified images are produced with an equal distribution using data augmentation. The performance of the proposed model is evaluated using a balanced dataset that consists of 5856 images. Achieved results were promising compared to those obtained by several previous studies. The model achieved a precision value of 98%, an area under curve (AUC) based on ROC of 97% and a loss value of 0.23.

[1] V. Chouhan, S. K. Singh, A. Khamparia et al., "A Novel Transfer Learning Based Approach for Pneumonia Detection in chest X-ray Images," Applied Sciences, vol. 10, no. 2, p. 559, 2020.

[2] A. Sharma, M. Negi, A. Goyal, R. Jain and P. Nagrath, "Detection of Pneumonia Using ML & DL in Python. IOP Conference Series: Materials Science and Engineering," Proc. of the 1st Int. Conf. on Computational Research and Data Analytics (ICCRDA 2020), Rajpura, India, DOI: 10.1088/1757-899X/1022/1/012066, 2021.

[3] C. J. Saul, D. Y. Urey and C. D. Taktakoglu, "Early Diagnosis of Pneumonia with Deep Learning," ArXiv. /abs/1904.00937, DOI: 10.48550/arXiv.1904.00937, 2019.

[4] R. Sarkar, A. Hazra, K. Sadhu and P. Ghosh, "A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks," Proc. of the Computer Vision and Machine Intelligence in Medical Image Analysis Conf., Part of the Advances in Intelligent Systems and Computing Book Series, vol. 992, DOI: 10.1007/978-981-13-8798-2_1, 2020.

[5] A. Tilve, S. Nayak, S. Vernekar, D. Turi, P.R. Shetgaonkar and S. Aswale, "Pneumonia Detection Using Deep Learning Approaches," Proc. of the Int. Conf. on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1-8, DOI: 10.1109/ic-ETITE47903.2020.152, 2020.

[6] D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan and A. Mittal, "Pneumonia Detection Using CNN Based Feature Extraction," Proc. of the IEEE Int. Conf. on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-7, DOI: 10.1109/ICECCT.2019.8869364, 2019.

[7] A. M. Alqudah, S. Qazan and I. S. Masad, "Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images," J. Medical and Biological Eng., vol. 41, pp. 599–609, DOI: 10.1007/s40846-021-00631-1, 2021.

[8] A. K. Jaiswal et al., "Identifying Pneumonia in Chest X-rays: A Deep Learning Approach," Measurement, vol. 145, pp. 511–518. DOI: 10.1016/j.measurement.2019.05.076, 2019.

[9] S. Kido, J. Ikezoe, H. Naito, S. Tamura and S. Machi, "Fractal Analysis of Interstitial Lung Abnormalities in Chest Radiography," Radiographics, vol. 15, no. 6, pp. 1457-1464, DOI: 10.1148/radiographics.15.6.8577968, 1995.

[10] T. Ishida, S. Katsuragawa, K. Ashizawa et al., "Application of Artificial Neural Networks for Quantitative Analysis of Image Data in Chest Radiographs for Detection of Interstitial Lung Disease," Journal of Digital Imaging, vol. 11, no. 4, pp. 182–192, DOI: 10.1007/BF03178081, 1998.

[11] T. Rafael et al., "Comparative Performance Analysis of Machine Learning Classifiers in Detection of Childhood Pneumonia Using Chest Radiographs," Procedia Computer Science, vol. 18, pp. 2579-2582, DOI: 10.1016/j.procs.2013.05.444, 2013.

[12] S. Reza, O. B. Amin and M. M. A. Hashem, "A Novel Feature Extraction and Selection Technique for Chest X-ray Image View Classification," Proc. of the 5th Int. Conf. on Advances in Electrical Engineering (ICAEE), pp. 189-194, DOI: 10.1109/ICAEE48663.2019.8975457, 2019.

[13] G. Verma and S. Prakash, "Pneumonia Classification using Deep Learning in Healthcare," Int. J. of Innovative Technology and Exploring Engineering (IJITEE), vol. 9, no. 4, pp. 1715–1723, 2020.

[14] N. Ansari, A. Faizabadi, S. Motakabber and M. Ibrahimy, "Effective Pneumonia Detection Using ResNet based Transfer Learning," Test Eng. and Management, vol. 82, pp. 15146 – 15153, 2020.

[15] K. Kadam, S. Ahirrao, H. Kaur, P. Shraddha and A. Pawar, "Deep Learning Approach for Prediction of Pneumonia," Int. J. of Scientific and Technology Research, vol. 8, no. 10, pp. 2986–2989, 2019.

[16] S. Yoo, I. Gujrathi, M. A. Haider and F. Khalvati, "Prostate Cancer Detection Using Deep Convolutional Neural Networks," Scientific Reports, vol. 9, p. 19518, DOI: 10.1038/s41598-019-55972-4, 2019.

[17] D. S. Kermany et al., "Identifying Medical Diagnoses and Treatable Diseases by Image-based Deep Learning," Cell, vol. 172, no. 5, pp. 1122-1131.e9, DOI: 10.1016/j.cell.2018.02.010, 2018.

[18] M. Toğaçar, B. Ergen, Z. Cömert and F. Özyurt, "A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models," IRBM, vol. 41, no. 4, pp. 212–222, DOI: 10.1016/j.irbm.2019.10.006, 2020.

[19] B. Almaslukh, "A Lightweight Deep Learning-Based Pneumonia Detection Approach for Energy-Efficient Medical Systems," Wireless Communications and Mobile Computing, DOI: 10.1155/2021/5556635, 2021.

[20] J. E. Luján-García et al., "A Transfer Learning Method for Pneumonia Classification and Visualization," Applied Sciences, vol. 10, no. 8, p. 2908, DOI: 10.3390/app10082908, 2020.

[21] S. Ben Atitallah, M. Driss, W. Boulila, A. Koubaa and H. ben Ghézala, "Fusion of Convolutional Neural Networks Based on Dempster–Shafer Theory for Automatic Pneumonia Detection from Chest X-ray Images," Int. J. of Imaging Systems and Technology, vol. 32, no. 2, pp. 658–672, DOI: 10.1002/ima.22653, 2022.

[22] K. T. Islam, S. Wijewickrema, A. M. Collins and S. O'Leary, "A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images," Proc. of the 15th Int. Conf. on Computer Vision Theory and Applications, pp. 286-293, DOI: 10.5220/0008927002860293, 2020.

[23] E.Ayan and H. M. Ünver, "Diagnosis of Pneumonia from Chest X-ray Images Using Deep Learning," Proc. of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1-5, DOI: 10.1109/EBBT.2019.8741582, 2019.

[24] S. M. H. Hussain, S. M. Raju and A. R. Ismail, "Predicting Pneumonia and Region Detection from X-ray Images Using Deep Neural Network," arXiv: 2101.07717, DOI: 10.48550/arXiv.2101.07717, 2021.

[25] S. Singh and B. K. Tripathi, "Pneumonia Classification Using Quaternion Deep Learning," Multimedia Tools and Applications, vol. 81, pp. 1743–1764, DOI: 10.1007/s11042-021-11409-7, 2022.

[26] L. Račić, T. Popović, S. čakić and S. Šandi, "Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network," Proc. of the 25th Int. Conf. on Information Technology (IT), pp. 1-4, DOI: 10.1109/IT51528.2021.9390137, 2021.

[27] R. Jain, P. Nagrath, G. Kataria, V. S Kaushik and J. Hemanth D., "Pneumonia Detection in Chest X-ray Images Using Convolutional Neural Networks and Transfer Learning," Measurement, vol. 165, p. 108046, DOI: 10.1016/j.measurement.2020.108046, 2020.

[28] C. Shorten and T. M. Khoshgoftaar, "A Survey on Image Data Augmentation for Deep Learning," Journal of Big Data, vol. 60, Article no. 60, DOI: 10.1186/s40537-019-0197-0, 2019.

[29] N. M. Elshennawy and D. M. Ibrahim, "Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-ray Images," Diagnostics, vol. 10, no. 9, p. 649, 2020.

[30] K. He, X. Zhang, S. Ren and J. Sun, "Identity Mappings in Deep Residual Networks," Proc. of European Conf. on Computer Vision (ECCV 2016), Part of the Lecture Notes in Computer Science, vol. 9908, DOI: 10.1007/978-3-319-46493-0_38, 2016.

[31] K. He, X. Zhang, Sh. Ren and J. Sun, "Deep Residual Learning for Image Recognition," Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 770-778. [Online], Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7780459, 2016.

[32] W. Shang, J. Chiu and K. Sohn, "Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units," Proc. of the 31st AAAI Conference on Artificial Intelligence (AAAI-17), vol. 31, no. 1, pp. 509-1516, DOI: 10.1609/aaai.v31i1.10759, 2017.

[33] J. Bjorck, C. Gomes, B. Selman and K. Q. Weinberger, "Understanding Batch Normalization," arXiv: 1806.02375, DOI: 10.48550/arXiv.1806.02375, 2018.

[34] A.Khan, A.Sohail, U. Zahoora and A. S. Qureshi, "A Survey of the Recent Architectures of Deep Convolutional Neural Networks," Artificial Intelligence Review, vol. 53, pp. 5455-5516, DOI: 10.1007/s10462-020-09825-6, 2020.

[35] A. Desarda, "Build a Custom ResNetV2 with the Desired Depth from Scratch," Towards Data Science, [Online], Available: https://towardsdatascience.com/build-a-custom-resnetv2-with-the-desired-depth-92892ec79d4b, 2020, Last Accessed 31/10/2022.

[36] G. Huang, Z. Liu, L. Van der Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, DOI: 10.1109/CVPR.2017.243, 2017.

[37] R. Kundu, R. Das, Z.W. Geem, G-T Han and R. Sarkar, "Pneumonia Detection in Chest X-ray Images Using an Ensemble of Deep Learning Models," PLoS ONE, vol. 16, no. 9, p. e0256630, DOI: 10.1371/journal.pone.0256630, 2021.

[38] K. El Asnaoui, Y. Chawki and A. Idri, "Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using Deep Learning," Proc. of the Artificial Intelligence and Blockchain for Future Cybersecurity Applications, Part of the Studies in Big Data Book Series, vol. 90, DOI:10.1007/978-3-030-74575-2_14, 2021.

[39] Q. Wu and F. Wang, "Concatenate Convolutional Neural Networks for Non-intrusive Load Monitoring across Complex Background," Energies, vol. 12, no. 8, p. 1572, DOI: 10.3390/en12081572, 2019.

[40] J. Brownlee, "How to Choose an Activation Function for Deep Learning," Machine Learning Mastery, [Online], Available: https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/, 2021, Last Accessed 31/10/2022.

[41] J. Collis, "Glossary of Deep Learning: Bias," Deeper Learning, [Online], Available https://medium.com/deeper-learning/glossary-of-deep-learning-bias-cf49d9c895e2, 2017, Last Accessed on 31/10/2022.

[42] P. Mooney, "Chest X-ray Images (Pneumonia)," Kaggle, [Online], Available: www.kaggle.com/paultim
othymooney/chest-xray-pneumonia, 2018, Last Accessed on 31/10/2022.

[43] M. Vakili, M. Ghamsari and M. Rezaei, "Performance Analysis and Comparison of Machine and Deep Learning Algorithms for IoT Data Classification," arXiv: 2001.09636v1, DOI: 10.48550/arXiv.2001.09636, 2020.

[44] G. Labhane, R. Pansare, S. Maheshwari, R. Tiwari and A. Shukla, "Detection of Pediatric Pneumonia from Chest X-ray Images using CNN and Transfer Learning," Proc. of the 3rd Int. Conf. on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 85-92, DOI: 10.1109/ICETCE48199.2020.9091755, 2020.

[45] A. Mabrouk, R.P. Díaz Redondo, A. Dahou, M. Abd Elaziz and M. Kayed, "Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks," Applied Sciences, vol.12, p. 6448, DOI: 10.3390/app12136448, 2022.

[46] T. Rahman et al., "Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray," Applied Sciences, vol. 10, p. 3233, DOI: 10.3390/app10093233, 2020.

[47] S. Pallawi and D. Kumar Singh, "Preview and Analysis of Deep Neural Network for Alzheimer’s Disease Classification using Brain Medical Resonance Imaging," Cognitive Computation and Systems, vol. 5, no. 1, pp. 1-13, DOI: 10.1049/ccs2.12072, 2022.

[48] D. K. Jain, T. Singh, P. Saurabh, D. Bisen, N. Sahu, J. Mishra and H. Rahman, "Deep Learning-aided Automated Pneumonia Detection and Classification Using CXR Scans," Computational Intelligence Neuroscience, vol. 2022, Article ID 7474304, DOI: 10.1155/2022/7474304, 2022.

[49] M. Trivedi and A. Gupta, "A Lightweight Deep Learning Architecture for the Automatic Detection of Pneumonia Using Chest X-ray Images," Multimedia Tools Applications, vol. 81, pp. 5515–5536, DOI: 10.1007/s11042-021-11807-x, 2022.

[50] P. Szepesi and L. Szilágyi, "Detection of Pneumonia Using Convolutional Neural Networks and Deep Learning," Biocybernetics and Biomedical Engineering, vol. 42, no. 3, pp. 1012-1022, DOI: 10.1016/j.bbe.2022.08.001, 2022.