Automatic detection of covid-19 based on CT Scan images using the convolution neural network
Main Article Content
Abstract
The 2019 coronavirus pandemic (Covid-19) has been declared a health emergency by WHO with the death rate steadily increasing worldwide, various efforts have been made to deal with this pandemic, from prediction to receiving medical imaging. CT Scan and chest X-Ray images have been proven to be accurate to help medical personnel diagnose COVID, in this paper, we propose a convolutional neural network (CNN) approach and the DenseNet transfer learning model series which aims to understand and find the best classification for COVID or Non-COVID detection. On CT scan chest images, we made two special models in the Descent series, then compared the CNNs in both models by calculating the Accuracy, Precision, Recall, and F1-Score values and presented the results in the confusion matrix. The testing framework is carried out on CNN and the first model of the DenseNet series uses adam optimization, the input function is 244x244x3, the soft-max function is applied as an activity with losses across entropy categories, epoch 50, and batch size for training and testing 16 while validation uses batch size 8, the EarlyStopping function also determined, From the test results, the CNN model is superior to the Densenet series of the first model with an accuracy of about 0.76 (76%), when testing the second model, we carried out the shifting, zooming process and changed the input function to 64x64x3, epoch 30 by adding 4 layers. The second model approach produces better accuracy than CNN and the first DenseNet series, but not as good as expected, based on the test results on the second model produces an accuracy of 0.90 (90%) on Densenet169, Densenet121 around 0.88 (88%) and last Densenet201 is about 0.83 83%), so it is superior to simple CNN models
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
References
[2] Y. Peng, Y.-X. Tang, S. Lee, Y. Zhu, R. M. Summers, and Z. Lu, “COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature.,” ArXiv, vol. 2, pp. 1–20, 2020.
[3] X. Chen et al., “Dynamic chest CT evaluation in three cases of 2019 novel coronavirus pneumonia,” Arch. Iran. Med., vol. 23, no. 4, pp. 277–280, 2020.
[4] D. Müller, I. S. Rey, and F. Kramer, “Automated Chest CT Image Segmentation of COVID- 19 Lung Infection based on 3D U-Net,” pp. 1–9, 2020.
[5] B. Liu, X. Gao, M. He, F. Lv, and G. Yin, “Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks,” medRxiv, p. 2020.05.11.20097907, 2020.
[6] S. Rajpal, N. Kumar, and A. Rajpal, “COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest-Ray Images,” vol. 2019, 2020.
[7] S. Vaid, R. Kalantar, and M. Bhandari, “Deep learning COVID-19 detection bias: accuracy through artificial intelligence,” Int. Orthop., vol. 44, no. 8, pp. 1539–1542, 2020.
[8] D. Ezzat, A. ell Hassanien, and H. A. Ella, “GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm,” pp. 1–29, 2020.
[9] M. Yamac, M. Ahishali, A. Degerli, S. Kiranyaz, M. E. H. Chowdhury, and M. Gabbouj, “Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images,” pp. 1–10, 2020.
[10] A. I. Khan, J. L. Shah, and M. M. Bhat, “CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images,” Comput. Methods Programs Biomed., vol. 196, 2020.
[11] Eduardo Soares, Plamen Angelov, Sarah Biaso, Michele Higa Froes, and Daniel Kanda Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS- CoV-2 identification,” Cold Spring Harbor Laboratory Press, 2020.
[12] M. Z. Islam, M. M. Islam, and A. Asraf, “A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus ( COVID-19 ) Us- ing X-ray Images,” no. June, pp. 1–20, 2020.
[13] A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman, and P. R. Pinheiro, “CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection,” IEEE Access, vol. 8, pp. 91916–91923, 2020.
[14] M. Polsinelli, L. Cinque, and G. Placidi, “A Light CNN for detecting COVID-19 from CT scans of the chest,” pp. 1–13, 2020.
[15] T. Majeed, R. Rashid, D. Ali, and A. Asaad, “Covid-19 Detection using CNN Transfer Learning from X-ray Images,” medRxiv, p. 2020.05.12.20098954, 2020.
[16] X. Wang et al., “A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT,” IEEE Trans. Med. Imaging, vol. 39, no. 8, pp. 2615– 2625, 2020.
[17] S. Albahli, “Efficient gan-based chest radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia,” Int. J. Med. Sci., vol. 17, no. 10, pp. 1439–1448, 2020.
[18] P. R. A. S. Bassi and R. Attux, “A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays,” Apr. 2020.
[19] T. Ozcan, “A Deep Learning Framework for Coronavirus Disease (COVID-19) Detection in X-Ray Images,” vol. 90, no. 352, 2020.
[20] A. Sufian, A. Ghosh, A. S. Sadiq, and F. Smarandache, “A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic,” J. Syst. Archit., vol. 108, no. January, p. 101830, Sep. 2020.
[21] “SARS-COV-2 Ct-Scan Dataset | Kaggle.” [Online]. Available: https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset. [Accessed: 30-Sep-2020].