Using deep convolutional neural network to diagnose covid-19 disease from CT scan images
محورهای موضوعی : نشریه بینالمللی هوش تصمیمrezvaneh azizi 1 , hamid abbasi 2
1 - Department of Computer Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran
2 - Department of Computer Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran
کلید واژه: CT scan images, convolutional neural network, Covid-19, modeling, deep learning,
چکیده مقاله :
This paper examines the application of soft computing techniques in detecting COVID-19 through CT scan image analysis. Convolutional deep learning models are employed, and their performance in disease detection is evaluated. In this study, we investigate the optimization of feature extraction methods from CT scan images and present the results of our experiments. We focus on designing a COVID-19 detection system using a convolutional neural network model supported by open-source software such as Keras, Python, Google Colab, Kaggle, and Visual Studio. The results of this study indicate that using deep convolutional networks with layered architecture can significantly enhance the accuracy of COVID-19 detection through CT scan images and be effective in faster and more accurate patient diagnosis.The results of this study show that the use of deep convolutional networks in image processing provides us with significantly high accuracy and it is possible to improve the diagnosis of Covid-19 from these images with appropriate layering
This paper examines the application of soft computing techniques in detecting COVID-19 through CT scan image analysis. Convolutional deep learning models are employed, and their performance in disease detection is evaluated. In this study, we investigate the optimization of feature extraction methods from CT scan images and present the results of our experiments. We focus on designing a COVID-19 detection system using a convolutional neural network model supported by open-source software such as Keras, Python, Google Colab, Kaggle, and Visual Studio. The results of this study indicate that using deep convolutional networks with layered architecture can significantly enhance the accuracy of COVID-19 detection through CT scan images and be effective in faster and more accurate patient diagnosis.The results of this study show that the use of deep convolutional networks in image processing provides us with significantly high accuracy and it is possible to improve the diagnosis of Covid-19 from these images with appropriate layering
M. Aslan, "CoviDetNet: A new
COVID-19 diagnostic system based on
deep features of chest x-ray," ElectricalElectronics Engineering, 2022.
[2] T. M. Y. E. B. U. Y. O. A. U. Ozturk T, "Automated detection of COVID-19 cases using deep neural networks with X-ray images.," Computers in Biology and Medicine., 2020.
[3] P. T. N. ,. I. ,. F. Linh T. Duong, "Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning," www.elsevier.com/locate/asoc, 2022.
[4] R. S. Shimpy Goyal, "Detection and classifcation of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques," Journal of Ambient Intelligence and Humanized Computing , 2021.
[5] Z. S. L. L. Z. X. Z. X. H. Z. e. a. Song Y, " Deep learning enables accurate diagnosis of novel coronavirus (COVID19) with CT images.," IEEE/ACM Transactions on Computational Biology and Bioinformatics., 2021.
[6] S. C. E. Marios Anthimopoulos, "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep," IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 35, NO. 5,, 2016.
[7] M. S. B. Maind, "Research Paper on Basic of Artificial Neural Network," International Journal on Recent and Innovation Trends in Computing and Communication , 2000.
[8] A. A. Khandaker Foysal Haque, "A Deep Learning Approach to Detect COVID-19 Patients from Chest X-ray Images," Proceedings of the 3rd IEEE International Conference on Computing, Electronics & Communications Engineering, University of Essex, Southend Campus, Southend, UK, , 2022.
[9] M. H. Olyaee, H. Abasi, M. Yaghoobi, Using Hierarchical Adaptive Neuro Fuzzy Systems And Design Two New Edge Detectors In Noisy Images, Journal of Soft Computing and Applications,2013, P.110.
[10] G. H. Y. B. Yann LeCun, "Deep learning REVIEW," doi:10.1038/nature14539
