Detection of COVID-19, Pneumonia, and Tuberculosis Using Convolutional Neural Networks and Ensemble of Deep Learning Architectures
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Abstract
Testing, diagnosis, and treatment must be done quickly in the current scenario, where the COVID-19 pandemic continues to affect the world's health. Pneumonia and Tuberculosis (TB) are always very concerning chest diseases, and COVID-19 has also been added to this list. Chest X-rays (CXR) and CT scans are major sources for diagnosing respiratory disorders. As CT scans are a little bit costlier and take more time, as well as expose patients to mild radiation, the use of CXR is increasing for the diagnosis of chest diseases. This study uses CXR images to accurately identify diseases using the developed MobileNetV2, ResNet50, VGG19, and DenResCov-19 Convolutional Neural Network (CNN). The models were analyzed and tested using the publicly available 13805, 5840, and 4200 chest x-ray images for COVID-19, pneumonia, and TB cases. The in-house mobile app employs a variety of deep-learning models to identify diseases from chest X-rays. With 99.79% accuracy and 96.89% F1-score, ResNet50 is the best model architecture for COVID-19 classification. The ResNet50 pneumonia classification model was 99.17% accurate and had an F1-score of 94.52%. With 99.88% TB detection accuracy and 99.64% F1-score, MobileNetV2 and ResNet50 were equivalent. Comparing model efficacy on the dataset was fascinating.
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References
Shen, D.; Wu, G.; Suk, H.-I. Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering 2017, 19(1), 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Yanase, J.; Triantaphyllou, E. A systematic survey of computer-aided diagnosis in medicine: Past and present developments. Expert Systems With Applications 2019, 138, 112821. https://doi.org/10.1016/j.eswa.2019.112821
Khan, M. A.; Quasim, M. T.; Alghamdi, N. S.; Khan, M. Y. A Secure Framework for Authentication and Encryption Using Improved ECC for IoT-Based Medical Sensor Data. IEEE Access 2020, 8, 52018–52027. https://doi.org/10.1109/access.2020.2980739
Sathitratanacheewin, S.; Sunanta, P.; Pongpirul, K. Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon 2020, 6(8), e04614. https://doi.org/10.1016/j.heliyon.2020.e04614
Gao, X. W.; James-Reynolds, C.; Currie, E. Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing 2019, 392, 233–244. https://doi.org/10.1016/j.neucom.2018.12.086
Hooda, R.; Mittal, A.; Sofat, S. Automated TB classification using ensemble of deep architectures. Multimedia Tools and Applications 2019, 78(22), 31515–31532. https://doi.org/10.1007/s11042-019-07984-5
Chandra, T. B.; Verma, K.; Singh, B. K.; Jain, D.; Netam, S. S. Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. Expert Systems With Applications 2020, 158, 113514. https://doi.org/10.1016/j.eswa.2020.113514
Lopes, U. K.; Valiati, J. F. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Computers in Biology and Medicine 2017, 89, 135–143. https://doi.org/10.1016/j.compbiomed.2017.08.001
Kumar, J. S.; Balamurugan, S. A. A.; Sasikala, S. Analysis of Deep Learning Techniques for Tuberculosis Disease. SN Computer Science 2021, 2(4). https://doi.org/10.1007/s42979-021-00680-y
Rahimzadeh, M.; Attar, A. A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked 2020, 19, 100360. https://doi.org/10.1016/j.imu.2020.100360
Luján-García, J. E.; Yáñez-Márquez, C.; Villuendas-Rey, Y.; Camacho-Nieto, O. A Transfer Learning Method for Pneumonia Classification and Visualization. Applied Sciences 2020, 10(8), 2908. https://doi.org/10.3390/app10082908
Stephen, O.; Sain, M.; Maduh, U. J.; Jeong, D.-U. An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. Journal of Healthcare Engineering 2019, 2019, 1–7. https://doi.org/10.1155/2019/4180949
Lascu, M.-R. Deep Learning in Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs on CXR and CT Images. Journal of Medical and Biological Engineering 2021, 41(4), 514–522. https://doi.org/10.1007/s40846-021-00630-2
Sirazitdinov, I.; Kholiavchenko, M.; Mustafaev, T.; Yixuan, Y.; Kuleev, R.; Ibragimov, B. Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Computers & Electrical Engineering 2019, 78, 388–399. https://doi.org/10.1016/j.compeleceng.2019.08.004
Asnaoui, K. E. Design ensemble deep learning model for pneumonia disease classification. International Journal of Multimedia Information Retrieval 2021, 10(1), 55–68. https://doi.org/10.1007/s13735-021-00204-7
Sirazitdinov, I.; Kholiavchenko, M.; Mustafaev, T.; Yixuan, Y.; Kuleev, R.; Ibragimov, B. Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Computers & Electrical Engineering 2019, 78, 388–399. https://doi.org/10.1016/j.compeleceng.2019.08.004
Goyal, S.; Singh, R. Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing 2021, 14(4), 3239–3259. https://doi.org/10.1007/s12652-021-03464-7
Rahmani, A. M.; Azhir, E.; Naserbakht, M.; Mohammadi, M.; Aldalwie, A. H. M.; Majeed, M. K.; Karim, S. H. T.; Hosseinzadeh, M. Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review. Multimedia Tools and Applications 2022, 81(20), 28779–28798. https://doi.org/10.1007/s11042-022-12952-7
Soomro, T. A.; Zheng, L.; Afifi, A. J.; Ali, A.; Yin, M.; Gao, J. Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research. Artificial Intelligence Review 2021, 55(2), 1409–1439. https://doi.org/10.1007/s10462-021-09985-z
Heidari, A.; Navimipour, N. J.; Unal, M.; Toumaj, S. The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions. Computers in Biology and Medicine 2021, 141, 105141. https://doi.org/10.1016/j.compbiomed.2021.105141
Gomes, R.; Kamrowski, C.; Langlois, J.; Rozario, P.; Dircks, I.; Grottodden, K.; Martinez, M.; Tee, W. Z.; Sargeant, K.; LaFleur, C.; Haley, M. A Comprehensive Review of Machine Learning Used to Combat COVID-19. Diagnostics 2022, 12(8), 1853. https://doi.org/10.3390/diagnostics12081853
Alafif, T.; Tehame, A. M.; Bajaba, S.; Barnawi, A.; Zia, S. Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions. International Journal of Environmental Research and Public Health 2021, 18(3), 1117. https://doi.org/10.3390/ijerph18031117
Shorten, C.; Khoshgoftaar, T. M.; Furht, B. Deep Learning applications for COVID-19. Journal of Big Data 2021, 8(1). https://doi.org/10.1186/s40537-020-00392-9
Rehman, A.; Iqbal, M. A.; Xing, H.; Ahmed, I. COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review. Applied Sciences 2021, 11(8), 3414. https://doi.org/10.3390/app11083414
Bekele, S. K.; Gutema, M. G.; Tujuba, E. D.; Jain, R.; Bekuma, Y. IoT-Deep Learning Based Face Mask Detection System for Entrance and Exit Door. International Journal of Electrical and Electronics Research 2022, 10(3), 751–759. https://doi.org/10.37391/ijeer.100356
Munadi, K.; Muchtar, K.; Maulina, N.; Pradhan, B. Image Enhancement for Tuberculosis Detection Using Deep Learning. IEEE Access 2020, 8, 217897–217907. https://doi.org/10.1109/access.2020.3041867
Balakrishnan, V.; Kherabi, Y.; Ramanathan, G.; Paul, S. A.; Tiong, C. K. Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review. Progress in Biophysics and Molecular Biology 2023, 179, 16–25. https://doi.org/10.1016/j.pbiomolbio.2023.03.001
Jaiswal, A. K.; Tiwari, P.; Kumar, S.; Gupta, D.; Khanna, A.; Rodrigues, J. J. P. C. Identifying pneumonia in chest X-rays: A deep learning approach. Measurement 2019, 145, 511–518. https://doi.org/10.1016/j.measurement.2019.05.076
Ahmed, M. S.; Rahman, A.; AlGhamdi, F.; AlDakheel, S.; Hakami, H.; AlJumah, A.; AlIbrahim, Z.; Youldash, M.; Khan, M. A. A.; Ahmed, M. I. B. Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach. Diagnostics 2023, 13(15), 2562. https://doi.org/10.3390/diagnostics13152562
Nahiduzzaman, Md.; Goni, M. O. F.; Islam, Md. R.; Sayeed, A.; Anower, Md. S.; Ahsan, M.; Haider, J.; Kowalski, M. Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture. Journal of Applied Biomedicine 2023, 43 (3), 528–550. https://doi.org/10.1016/j.bbe.2023.06.003
Ali, M. U.; Kallu, K. D.; Masood, H.; Tahir, U.; Gopi, C. V. V. M.; Zafar, A.; Lee, S. W. A CNN-Based Chest Infection Diagnostic Model: A Multistage Multiclass Isolated and Developed Transfer Learning Framework. International Journal of Intelligent Systems 2023, 2023, 1–12. https://doi.org/10.1155/2023/6850772
Kabir, M. M.; Mridha, M. F.; Rahman, A.; Hamid, Md. A.; Monowar, M. M. Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation. Heliyon 2024, 10(5), e26801. https://doi.org/10.1016/j.heliyon.2024.e26801
Kaewlek, T.; Tanyong, K.; Chakkaeo, J.; Kladpree, S.; Chusin, T.; Yabsantia, S.; Udee, N. Classification of Pneumonia, Tuberculosis, and COVID-19 on Computed Tomography Images using Deep Learning. Trends in Sciences 2023, 20(11), 6974. https://doi.org/10.48048/tis.2023.6974
Singh, S.; Kumar, M.; Kumar, A.; Verma, B. K.; Abhishek, K.; Selvarajan, S. Efficient pneumonia detection using Vision Transformers on chest X-rays. Scientific Reports 2024, 14(1). https://doi.org/10.1038/s41598-024-52703-2
Monowar, K. F.; Hasan, Md. A. M.; Shin, J. Lung Opacity Classification With Convolutional Neural Networks Using Chest X-rays; 11th International Conference on Electrical and Computer Engineering (ICECE), IEEE, 2020. https://doi.org/10.1109/icece51571.2020.9393135
Oğuz, Ç.; Yağanoğlu, M. Detection of COVID-19 using deep learning techniques and classification methods. Information Processing & Management 2022, 59(5), 103025. https://doi.org/10.1016/j.ipm.2022.103025
Hussain, E.; Hasan, M.; Rahman, M. A.; Lee, I.; Tamanna, T.; Parvez, M. Z. CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos Solitons & Fractals 2020, 142, 110495. https://doi.org/10.1016/j.chaos.2020.110495
COVID-19 Radiography Database. Kaggle. https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database (accessed 2024-02-19).
Chest X-Ray Images (Pneumonia). Kaggle. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia/code?resource=download (accessed 2024-02-17).
Tuberculosis (TB) Chest X-ray Database. Kaggle. https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset (accessed 2024-02-10)
Rahman, M. M.; Manik, Md. M. H.; Islam, Md. M.; Mahmud, S.; Kim, J.-H. An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network. 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) 2020. https://doi.org/10.1109/iemtronics51293.2020.9216386
Loey, M.; Manogaran, G.; Taha, M. H. N.; Khalifa, N. E. M. A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement 2020, 167, 108288. https://doi.org/10.1016/j.measurement.2020.108288