Power, Control, and Data Processing Systems

Power, Control, and Data Processing Systems

Transfer Learning-based Detection of COVID-19 Cases from Chest CT Scans

Document Type : Original Research

Authors
Safiye Ghasemi 1
Somayeh Ghasemi 2
Amir Masoud Rahmani 3
Rohollah Barzamini 4
1 Department of computer, Sep.C., Islamic Azad University, Sepidan, Iran
2 Department of computer, Christ School, London, UK
3 Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan
4 Department of Electrical Engineering, CT.C. Islamic Azad University, Tehran, Iran.
10.30511/pcdp.2025.2063435.1030
Abstract
When taking into account the prevailing COVID-19 pandemic scenario, by early detecting COVID-19, we can formulate an effective treatment plan and make decisions regarding disease containment. As a result of this issue, Artificial Intelligence (AI) specialists have been encouraged to develop models that employ deep learning techniques in COVID-19 detection. These models diagnose infection severity rapidly and economically. The present study proposes a Deep Convolutional Neural Network (CNN) model based on PSO, which helps identify COVID-19 infections by chest Computerized Tomography (CT) scans. Moreover, we demonstrate how pre-trained models can classify the disease through transfer learning. Initially, the random search is used to identify an optimal CNN model. The transfer learning strategy presents an analysis of several popular pre-trained models. The optimal CNN model inherits several layers from these previously trained models, and we then fine-tune the selected optimal CNN model accordingly. The proposed architecture is built using three pre-trained models with the highest quality. PSO algorithm is applied to estimate how each pre-trained model will affect the ultimate detection of the suggested model. To train the model, we analyzed two publicly available datasets—COVID-CT and SARS-CoV-2—applying distinct pre-processing techniques to each. According to the experimental results, our PSO-based configuration optimization performed well on this dataset and can achieve better results with more training data. As a result of extensive parameter tuning, the proposed model can identify COVID-19 with an accuracy of up to 90.32%. This model will facilitate the detection and diagnosis of COVID-19 promptly.
Keywords
Computerized Tomography (CT) Scans
Convolutional Neural Network (CNN)
Coronavirus
Deep learning
Pre-trained models
Transfer learning
Particle Swarm Optimization (PSO)

Subjects


[1]    Z. Ullah, M. Usman and J. Gwak, "MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images," Expert Systems with Applications, p. 119475, 2023. 
[2]    N. S. Shaik and T. K. Cherukuri, "Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans," Computers in Biology and Medicine 141, p. 105127, 2022. 
[3]    N. N. Das, N. Kumar, M. Kaur, V. Kumar and D. Singh, "Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays," Irbm 43, vol. no. 2, pp. 114-119, 2022. 
[4]    C. Ouchicha, O. Ammor and M. Meknassi, "CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images," Chaos, Solitons & Fractals, no. 140, p. 110245, 2020. 
[5]    F. Ali, F. Ullah, J. I. Khan, J. Khan, A. W. Sardar and S. Lee, "COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm," Chaos, Solitons & Fractals, no. 167, p. 112984, 2023. 
[6]    M. Rahimzadeh, A. Attar and S. M. Sakhaei, "A fully automated deep learning-based network for detecting COVID-19 from a new and large lung CT scan dataset," Biomedical Signal Processing and Control 68, p. 102588, 2021. 
[7]    K. Gupta and V. Bajaj, "Deep learning models-based CT-scan image classification for automated screening of COVID-19," Biomedical Signal Processing and Control, vol. 80, p. 104268, 2023. 
[8]    M. F. Aslan, M. F. Unlersen, K. Sabanci and A. Durdu, "CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection," Applied Soft Computing98, p. 106912, 2021. 
[9]    A. Altan and S. Karasu, "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, solitons & fractals, no. 140, p. 110071, 2020. 
[10]    E. F. Ohata, G. M. Bezerra, J. V. S. d. Chagas, A. V. L. Neto, A. B. Albuquerque, V. H. C. D. Albuquerque and P. P. R. Filho, "Automatic detection of COVID-19 infection using chest X-ray images through transfer learning," IEEE/CAA Journal of Automatica Sinica 8, vol. no. 1, pp. 239-248, 2020. 
[11]    H. Panwar, P. K. Gupta, M. K. Siddiqui, R. Morales-Menendez and V. Singh, "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.," Chaos, Solitons & Fractals, no. 138, p. 109944, 2020. 
[12]    F. E. F. Junior and G. G. Yen, "Particle swarm optimization of deep neural networks architectures for image classification," Swarm and Evolutionary Computation, vol. 49, pp. 62-74, 2019. 
[13]    S. B. Nettur, S. Karpurapu, U. Nettur, L. S. Gajja, S. Myneni, A. Dusi and L. Posham, "A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images."," arXiv preprint arXiv:2501.17160, 2025. 
[14]    A. M. Ali, K. Ghafoor, A. Mulahuwaish and H. Maghdid, "COVID-19 pneumonia level detection using deep learning algorithm and transfer learning," Evolutionary Intelligence, vol. 2, no. 17, pp. 1035-1046, 2024. 
[15]    M.-L. Huang and Y.-C. Liao, "A lightweight CNN-based network on COVID-19 detection using X-ray and CT images," Computers in Biology and Medicine, vol. 146, p. 105604, 2022. 
[16]    N. D. Kathamuthu, S. Subramaniam, Q. H. Le, S. Muthusamy, H. Panchal, S. C. M. Sundararajan, A. J. Alrubaie and M. M. A. Zahra, "A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications," Advances in Engineering Software, no. 175, p. 103317, 2023. 
[17]    S. Hassantabar, M. Ahmadi and A. Sharifi, "Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches," Chaos, Solitons & Fractals, vol. 140, p. 110170, 2020. 
[18]    M. K. Mahbub, M. Biswas, L. Gaur, F. Alenezi and K. C. Santosh, "Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis," Information Sciences, vol. 592, pp. 389-4, 2022. 
[19]    M. Goncharov, M. Pisov, A. Shevtsov, B. Shirokikh, A. Kurmukov, I. Blokhin and V. C. e. al, "CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification," Medical image analysis, vol. 71, p. 102054, 2021. 
[20]    E. Hussain, M. Hasan, M. A. Rahman, I. Lee, T. Tamanna and M. Z. Parvez, "CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images," Chaos, Solitons & Fractals, no. 142, p. 110495, 2021. 
[21]    A. K. Das, S. Kalam, C. Kumar and D. Sinha, "TLCoV-An automated Covid-19 screening model using Transfer Learning from chest X-ray images.," Chaos, Solitons & Fractals, no. 144, p. 110713, 2021. 
[22]    A. Saygılı, "A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods," Applied Soft Computing, vol. 105, p. 107323, 2021. 
[23]    A. Zeroual, F. Harrou, A. Dairi and Y. Sun, "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, solitons & fractals, no. 140, p. 110121, 2020. 
[24]    J. Zhao, Y. Zhang, X. He and P. Xie, "Covid-ct-dataset: a ct scan dataset about covid-19," arXiv preprint arXiv:2003.13865 490, 2020. 
[25]    E. Soares, P. Angelov, S. Biaso, M. H. Froes and D. K. Abe, "SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification," MedRxiv, 2020. 
[26]    A. Kebaili, J. Lapuyade-Lahorgue and S. Ruan, "Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review," ournal of Imaging, vol. no. 4, no. 9, p. 81, 2023. 
[27]    T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung and a. X. C. Alec Radford, "Improved techniques for training gans," Advances in neural information processing systems, vol. 29, 2016. 
[28]    M. Canayaz, "C+ EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet," Chaos, Solitons & Fractals, no. 151, p. 111310, 2021. 
[29]    M. Shafiq and Z. Gu, "Deep residual learning for image recognition: a survey," Applied Sciences, vol. no. 18, no. 12, p. 8972, 2022. 
[30]    S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in International conference on machine learning, pmlr, 2015. 
[31]    A. Chatzi and O. Doody, "The one-way ANOVA test explained," Nurse researcher , vol. 32, no. 4, 2024. 
[32]    J. Demšar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine learning research, vol. 7, no. Jan, pp. 1-30, 2006. 
[33]    W. M. Shaban, A. H. Rabie, A. I. Saleh and M. A. Abo-Elsoud, "A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier," Knowledge-Based Systems, vol. 205, p. 106270, 2020. 
[34]    A. K. Mishra, S. K. Das, P. Roy and S. Bandyopadhyay, "Identifying COVID19 from chest CT images: a deep convolutional neural networks based approach," Journal of Healthcare Engineering, 2020. 
[35]    X. X. H. J. Z. Y. Z. Yang, S. Zhang and P. Xie., "COVID-CT-Dataset: a CT scan dataset about COVID-19," arXiv preprint arXiv:2003.13865, 2020. 
[36]    P. Afshar, S. Heidarian, N. Enshaei, F. Naderkhani, M. J. Rafiee, A. Oikonomou, F. B. Fard, K. Samimi, K. N. Plataniotis and A. Mohammadi, "COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning," Scientific Data 8, no. 1, p. 121, 2021. 
[37]    A. Sadeghi, M. Sadeghi, A. Sharifpour, M. Fakhar, Z. Zakariaei, M. Sadeghi, M. Rokni, A. Zakariaei, E. S. Banimostafavi and F. Hajati, "Potential diagnostic application of a novel deep learning-based approach for COVID-19," Scientific Reports 14, no. 1, p. 280, 2024. 
[38]    R. Rajpoot, M. Gour, S. Jain and V. B. Semwal, "Integrated ensemble CNN and explainable AI for COVID-19 diagnosis from CT scan and X-ray images," Scientific Reports 14, no. 1, p. 24985, 2024. 
[39]    I. M. Pires, F. Hussain, N. M. Garcia, P. Lameski and E. Zdravevski, "Homogeneous data normalization and deep learning: A case study in human activity classification," Future Internet 12, no. 11, p. 194, 2020. 
[40]    H. Alhichri, "CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images," Computers, Materials & Continua 67, no. 3, pp. 115-123, 2021. 
[41]    K. R. Bhatele, A. Jha, D. Tiwari, M. Bhatele, S. Sharma, M. R. Mithora and S. Singhal, "Covid-19 detection: A systematic review of machine and deep learning-based approaches utilizing chest x-rays and ct scans," Cognitive Computation 16, no. 4, pp. 1889-1926, 2024. 
[42]    T. G. Dietterich, "Ensemble methods in machine learning," in International workshop on multiple classifier systems, Berlin, Heidelberg, 2000. 
[43]    V. K. Verma, K. Saxena and U. Banodha, "Analysis Effect of K Values Used in K Fold Cross Validation for Enhancing Performance of Machine Learning Model with Decision Tree," in In International Advanced Computing Conference, Switzerland, 2023. 
Power, Control, and Data Processing Systems
Volume 2, Issue 3
Summer 2025
Pages 33-47

  • Receive Date 11 June 2025
  • Revise Date 24 July 2025
  • Accept Date 25 July 2025
  • First Publish Date 25 July 2025
  • Publish Date 01 September 2025