A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images
DOI:
https://doi.org/10.22399/ijcesen.425Keywords:
Lightweight, ECA, Attention MechanismAbstract
COVID-19 has affected hundreds of millions of individuals, seriously harming the global population’s health, welfare, and economy. Furthermore, health facilities are severely overburdened due to the record number of COVID-19 cases, which makes prompt and accurate diagnosis difficult. Automatically identifying infected individuals and promptly placing them under special care is a critical step in reducing the burden of such issues. Convolutional Neural Networks (CNN) and other machine learning techniques can be utilized to address this demand. Many existing Deep learning models, albeit producing the intended outcomes, were developed using millions of parameters, making them unsuitable for use on devices with constrained resources. Motivated by this fact, a novel lightweight deep learning model based on Efficient Channel Attention (ECA) module and SqueezeNet architecture, is developed in this work to identify COVID-19 patients from chest X-ray and CT images in the initial phases of the disease. After the proposed lightweight model was tested on different datasets with two, three and four classes, the results show its better performance over existing models. The outcomes shown that, in comparison to the current heavyweight models, our models reduced the cost and memory requirements for computing resources dramatically, while still achieving comparable performance. These results support the notion that proposed model can help diagnose Covid-19 in patients by being easily implemented on low-resource and low-processing devices.
References
Zhang, W. (2020). Imaging changes of severe COVID-19 pneumonia in advanced stage. Intensive care medicine, 46(5), 841-843.
Chen, X., Laurent, S., Onur, O. A., Kleineberg, N. N., Fink, G. R., Schweitzer, F., & Warnke, C. (2021). A systematic review of neurological symptoms and complications of COVID-19. Journal of neurology, 268, 392-402.
McIntosh, K. (2002). Community-acquired pneumonia in children. New England Journal of Medicine, 346(6), 429-437.
Goyal, S., & Singh, R. (2023). Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. Journal of Ambient Intelligence and Humanized Computing, 14(4), 3239-3259.
Gattinoni, L., Chiumello, D., Caironi, P., Busana, M., Romitti, F., Brazzi, L., & Camporota, L. (2020). COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive care medicine, 46, 1099-1102.
Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. (2020). Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Medical image analysis, 65, 101794.
Haghanifar, A., Majdabadi, M. M., Choi, Y., Deivalakshmi, S., & Ko, S. (2022). Covid-cxnet: Detecting covid-19 in frontal chest x-ray images using deep learning. Multimedia tools and applications, 81(21), 30615-30645.
Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020). Detection of SARS-CoV-2 in different types of clinical specimens. Jama, 323(18), 1843-1844.
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., ... & Shan, H. (2020). CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 295(1), 202-207.
Hertel, R., & Benlamri, R. (2022). A deep learning segmentation-classification pipeline for x-ray-based covid-19 diagnosis. Biomedical Engineering Advances, 3, 100041.
Thakur, S., Kasliwal, Y., Kothambawala, T., & Katarya, R. (2022). A Study on pulmonary image screening for the detection of COVID-19 using convolutional neural networks. In Data Engineering for Smart Systems: Proceedings of SSIC 2021 (pp. 461-468). Springer Singapore.
Hiremath, A., Viswanathan, V. S., Bera, K., Shiradkar, R., Yuan, L., Armitage, K., ... & Madabhushi, A. (2024). Deep Learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study. Computers in Biology and Medicine, 108643.
Ieracitano, C., Mammone, N., Versaci, M., Varone, G., Ali, A. R., Armentano, A., ... & Morabito, F. C. (2022). A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images. Neurocomputing, 481, 202-215.
Subramanian, N., Elharrouss, O., Al-Maadeed, S., & Chowdhury, M. (2022). A review of deep learning-based detection methods for COVID-19. Computers in Biology and Medicine, 143, 105233.
Agnihotri, A., & Kohli, N. (2023). Challenges, opportunities, and advances related to COVID-19 classification based on deep learning. Data Science and Management, 6(2), 98-109.
Akhtar, F., Heyat, M. B. B., Li, J. P., Patel, P. K., & Guragai, B. (2020, December). Role of machine learning in human stress: a review. In 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 170-174). IEEE.
Ukwuoma, C. C., Zhiguang, Q., Bin Heyat, M. B., Ali, L., Almaspoor, Z., & Monday, H. N. (2022). Recent advancements in fruit detection and classification using deep learning techniques. Mathematical Problems in Engineering, 2022(1), 9210947.
Guragai, B., AlShorman, O., Masadeh, M., & Heyat, M. B. B. (2020, December). A survey on deep learning classification algorithms for motor imagery. In 2020 32nd international conference on microelectronics (ICM) (pp. 1-4). IEEE.
AlShorman, O., Masadeh, M., Heyat, M. B. B., Akhtar, F., Almahasneh, H., Ashraf, G. M., & Alexiou, A. (2022). Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection. Journal of integrative neuroscience, 21(1), 20.
Bin Heyat, M. B., Akhtar, F., Abbas, S. J., Al-Sarem, M., Alqarafi, A., Stalin, A., ... & Wu, K. (2022). Wearable flexible electronics based cardiac electrode for researcher mental stress detection system using machine learning models on single lead electrocardiogram signal. Biosensors, 12(6), 427.
Teelhawod, B. N., Akhtar, F., Heyat, M. B. B., Tripathi, P., Mehrotra, R., Asfaw, A. B., ... & Masadeh, M. (2021, October). Machine learning in E-health: a comprehensive survey of anxiety. In 2021 International Conference on Data Analytics for Business and Industry (ICDABI) (pp. 167-172). IEEE.
Heyat, M. B., Akhtar, F., Khan, M. H., Ullah, N., Gul, I., Khan, H., & Lai, D. (2021). Detection, treatment planning, and genetic predisposition of bruxism: a systematic mapping process and network visualization technique. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders), 20(8), 755-775.
Bin Heyat, M. B., Akhtar, F., Khan, A., Noor, A., Benjdira, B., Qamar, Y., ... & Lai, D. (2020). A novel hybrid machine learning classification for the detection of bruxism patients using physiological signals. Applied Sciences, 10(21), 7410.
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022). COVID-19 image classification using deep learning: Advances, challenges and opportunities. Computers in Biology and Medicine, 144, 105350.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Li, Y., Daho, M. E. H., Conze, P. H., Zeghlache, R., Le Boité, H., Tadayoni, R., ... & Quellec, G. (2024). A review of deep learning-based information fusion techniques for multimodal medical image classification. Computers in Biology and Medicine, 108635.
Hassantabar, S., Wang, Z., & Jha, N. K. (2021). SCANN: Synthesis of compact and accurate neural networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(9), 3012-3025.
Hassantabar, S., Dai, X., & Jha, N. K. (2019). STEERAGE: Synthesis of neural networks using architecture search and grow-and-prune methods. arXiv preprint arXiv:1912.05831.
Bullock, J., Luccioni, A., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. Journal of Artificial Intelligence Research, 69, 807-845.
Al-Antari, M. A., Al-Masni, M. A., Choi, M. T., Han, S. M., & Kim, T. S. (2018). A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. International journal of medical informatics, 117, 44-54.
Al-Masni, M. A., Al-Antari, M. A., Park, J. M., Gi, G., Kim, T. Y., Rivera, P., ... & Kim, T. S. (2018). Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Computer methods and programs in biomedicine, 157, 85-94.
Gour, M., & Jain, S. (2022). Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification. Computers in biology and medicine, 140, 105047.
Zhu, X., Lyu, S., Wang, X., & Zhao, Q. (2021). TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 2778-2788).
Vaswani, A. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database.
https://www.kaggle.com/datasets/anasmohammedtahir/covidqu.
https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.
Roy, S., Tyagi, M., Bansal, V., & Jain, V. (2022). Svd-clahe boosting and balanced loss function for covid-19 detection from an imbalanced chest x-ray dataset. Computers in Biology and Medicine, 150, 106092.
Ning, W., Lei, S., Yang, J., Cao, Y., Jiang, P., Yang, Q., ... & Wang, Z. (2020). iCTCF: an integrative resource of chest computed tomography images and clinical features of patients with COVID-19 pneumonia.
Islam, M. N., Hasan, M., Masum, A. K. M., Uddin, M. Z., & Alam, M. G. R. (2021, December). Demystify the black-box of deep learning models for covid-19 detection from chest ct radiographs. In 2021 24th International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE.
Özyurt, F., Sert, E., & Avcı, D. (2020). An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Medical hypotheses, 134, 109433.
Jadhav, P., Rajguru, G., Datta, D., & Mukhopadhyay, S. (2020). Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybernetics and Biomedical Engineering, 40(1), 494-504.
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141).
Shu, X., Chang, F., Zhang, X., Shao, C., & Yang, X. (2022). ECAU-Net: Efficient channel attention U-Net for fetal ultrasound cerebellum segmentation. Biomedical Signal Processing and Control, 75, 103528.
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2020). ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11534-11542).
Shi, Y., Wang, Z., Du, X., Ling, G., Jia, W., & Lu, Y. (2022). Research on the membrane fouling diagnosis of MBR membrane module based on ECA-CNN. Journal of Environmental Chemical Engineering, 10(3), 107649.
Lin, X., Huang, Q., Huang, W., Tan, X., Fang, M., & Ma, L. (2021). Single image deraining via detail-guided efficient channel attention network. Computers & Graphics, 97, 117-125.
George, G. S., Mishra, P. R., Sinha, P., & Prusty, M. R. (2023). COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network. Biocybernetics and Biomedical Engineering, 43(1), 1-16.
Jyoti, K., Sushma, S., Yadav, S., Kumar, P., Pachori, R. B., & Mukherjee, S. (2023). Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images. Computers in Biology and Medicine, 152, 106331.
Malik, D., Anjum, & Katarya, R. (2022). Comparative analysis by transfer learning of pre-trained models for detection of covid-19 using chest x-ray images. In Proceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences: PCCDS 2021 (pp. 549-557). Springer Singapore.
Ukwandu, O., Hindy, H., & Ukwandu, E. (2022). An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics. Healthcare Analytics, 2, 100096.
Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2022). An efficient deep learning method for detection of COVID-19 infection using chest X-ray images. Diagnostics, 13(1), 131.
Hussein, H. I., Mohammed, A. O., Hassan, M. M., & Mstafa, R. J. (2023). Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images. Expert Systems with Applications, 223, 119900.
Pradeep Dalvi, P., Reddy Edla, D., Purushothama, B. R., & Dharavath, R. (2024). COVID-19 detection from Chest X-ray images using a novel lightweight hybrid CNN architecture. Multimedia Tools and Applications, 1-23.
Ainapure, B. S., Appasani, B., Schiopu, A. G., Oproescu, M., & Bizon, N. (2024). A Lightweight Deep Learning Model and Web Interface for COVID-19 Detection Using Chest X-Rays. Traitement du Signal, 41(1).
Wang, S., Ren, J., & Guo, X. (2024). A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection. PloS one, 19(6), e0303049.
Asif, S., Zhao, M., Tang, F., & Zhu, Y. (2024). LWSE: a lightweight stacked ensemble model for accurate detection of multiple chest infectious diseases including COVID-19. Multimedia Tools and Applications, 83(8), 23967-24003.
Huang, M. L., & Liao, Y. C. (2022). A lightweight CNN-based network on COVID-19 detection using X-ray and CT images. Computers in Biology and Medicine, 146, 105604.
Ahamed, K. U., Islam, M., Uddin, A., Akhter, A., Paul, B. K., Yousuf, M. A., ... & Moni, M. A. (2021). A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images. Computers in biology and medicine, 139, 105014.
Sanida, T., Sideris, A., Tsiktsiris, D., & Dasygenis, M. (2022). Lightweight neural network for COVID-19 detection from chest X-ray images implemented on an embedded system. Technologies, 10(2), 37.
Asham, M. A., Al-Shargabi, A. A., Al-Sabri, R., & Meftah, I. (2024). A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays. Multimedia Tools and Applications, 1-29.
Asif, S., Ain, Q. U., Al-Sabri, R., & Abdullah, M. (2024). LitefusionNet: Boosting the Performance for Medical Image Classification with an Intelligent and Lightweight Feature Fusion Network. Journal of Computational Science, 102324.
Soleimani-Fard, S., & Ko, S. B. (2024). Res-MGCA-SE: a lightweight convolutional neural network based on vision transformer for medical image classification. Neural Computing and Applications, 1-14.
Fang, L., & Wang, X. (2022). COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images. biocybernetics and biomedical engineering, 42(3), 977-994.
Sarv Ahrabi, S., Scarpiniti, M., Baccarelli, E., & Momenzadeh, A. (2021). An accuracy vs. complexity comparison of deep learning architectures for the detection of COVID-19 disease. Computation, 9(1), 3.
Yasar, H. and Ceylan, M., 2024. Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images. Cognitive Computation, 16(4), pp.1806-1833.
Hassan, E., Shams, M.Y., Hikal, N.A. and Elmougy, S., 2024. Detecting COVID-19 in chest CT images based on several pre-trained models. Multimedia Tools and Applications, pp.1-21.
Zhang, H., Lv, Z., Liu, S., Sang, Z. and Zhang, Z., 2024. Cn2a-capsnet: a capsule network and CNN-attention based method for COVID-19 chest X-ray image diagnosis. Discover Applied Sciences, 6(4), p.190.
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