Automatic detection of covid-19 based on CT Scan images using the convolution neural network

Main Article Content

Mawaddah Harahap
Masdiana Damanik
Linda Wati
Wahyudi Valentino Simamora
Isnaeni Khairani Sipahutar
Amir Mahmud Husein

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

Download data is not yet available.

Article Details

How to Cite
[1]
M. Harahap, M. Damanik, L. Wati, W. V. Simamora, I. K. Sipahutar, and A. M. Husein, “Automatic detection of covid-19 based on CT Scan images using the convolution neural network”, INFOTEL, vol. 13, no. 4, Dec. 2021.
Section
Informatics

References

[1] S. Wang et al., “A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis,” Eur. Respir. J., vol. 56, no. 2, p. 2000775, 2020.
[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].