Klasifikasi Virus SARS-CoV-2 Berdasarkan Citra CT-Scan Menggunakan Convolutional Neural Network

INFOTEL : Jurnal INFOTEL [ISSN Print 2085-3688] [ISSN Online 2460-0997]
1 Desember 2022 oleh
Klasifikasi Virus SARS-CoV-2 Berdasarkan Citra CT-Scan Menggunakan Convolutional Neural Network
kelvin leonardi

Abstrak


Purpose: Convolutional Neural Network (CNN) is one of the most popular and widely used deep learning algorithms. These algorithms are commonly used in various applications, including image processing in medical and digital forensics, speech recognition, and other academic disciplines. SARS-CoV-2 (COVID-19) is a disease that first appeared in Wuhan, China, and has symptoms similar to pneumonia. This study aims to classify the covid-19 virus by proposing a deep learning model to prevent infection rates.

Methods: The dataset used in this study is a public dataset originating from a hospital in Sao Paulo, Brazil. The data images consisted of 1252 infected with covid and 1230 data classified as non-covid but have other lung diseases. The classification method proposed in this research is a CNN model based on Resnet 50.

Result: The experimental results show that the proposed Resnet 50-based convolutional neural network model works well in classifying SARS-CoV-2 disease using CT-Scan images. Our proposed model obtains 95% accuracy, precision, recall, and f1 values on the Epoch 500.

Novelty: In this experiment, we utilized the Resnet50-based CNN model to classify the SARS-CoV-2 (COVID-19) disease using CT-Scan images and got good performance.

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Kelvin Leonardi Kohsasih

Universitas Potensi Utama

B. Herawan Hayadi

Universitas Potensi Utama

V. Maeda-Gutiérrez et al., “Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases,” Appl. Sci., vol. 10, no. 4, pp. 1–15, 2020.

H. Alaskar and T. Saba, “Machine Learning and Deep Learning: A Comparative Review,” in Proc. Integr. Intell. Enable Netw. Comput., 2021, pp. 143–150.

B. Diallo, T. Urruty, P. Bourdon, and C. Fernandez-Maloigne, “Robust Forgery Detection for Compressed Images Using CNN Supervision,” Forensic Sci. Int. Reports, vol. 2, p. 100112, 2020.

A. Alsobhani, H. M. A. ALabboodi, and H. Mahdi, “Speech Recognition Using Convolution Deep Neural Networks,” J. Phys. Conf. Ser., vol. 1973, no. 1, p. 12166, 2021.

A. B. Nassif, I. Shahin, I. Attili, M. Azzeh, and K. Shaalan, “Speech Recognition Using Deep Neural Networks: A Systematic Review,” IEEE Access, vol. 7, pp. 19143–19165, 2019.

A. A. Reshi et al., “An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification,” Complexity, vol. 2021, p. 6621607, 2021.

S. N. Pasha, D. Ramesh, S. Mohmmad, A. Harshavardhan, and Shabana, “Cardiovascular Disease Prediction Using Deep Learning Techniques,” in IOP Conf. Ser.: Mater. Sci. Eng., 2020, vol. 981, no. 2, p. 22006.

N. Zakaria, F. Mohamed, R. Abdelghani, and K. Sundaraj, “VGG16, ResNet-50, and GoogLeNet Deep Learning Architecture for Breathing Sound Classification: A Comparative Study,” in 2021 Int. Conf. Artif. Intell. Cyber Secur. Syst. Priv. (AI-CSP), 2021, pp. 1–6.

D. M. Midyanti, S. Bahri, and R. Hidayati, “Diagnosis of Lung Disease Using Learning Vector Quantization 3 ( LVQ3 ),” Sci. J. Informatics, vol. 7, no. 2, pp. 173–179, 2020.

D. C. Febrianto, I. Soesanti, and H. A. Nugroho, “Convolutional Neural Network for Brain Tumor Detection,” in IOP Conf. Ser.: Mater. Sci. Eng., 2020, vol. 771, no. 1, p. 12031.

M. Bukhari, S. Yasmin, S. Sammad, and A. A. Abd El-Latif, “A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning,” Math. Probl. Eng., vol. 2022, p. 2801227, 2022.

M. Ghaderzadeh and F. Asadi, “Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review,” J. Healthc. Eng., vol. 2021, p. 6677314, 2021.

T. B. Alakus and I. Turkoglu, “Comparison of Deep Learning Approaches to Predict COVID-19 Infection,” Chaos, Solitons & Fractals, vol. 140, p. 110120, 2020.

N. Chen et al., “Epidemiological and Clinical Characteristics of 99 Cases of 2019 Novel Coronavirus Pneumonia in Wuhan, China: a Descriptive Study,” Lancet, vol. 395, no. 10223, pp. 507–513, 2020.

F. Rustam et al., “COVID-19 Future Forecasting Using Supervised Machine Learning Models,” IEEE Access, vol. 8, pp. 101489–101499, 2020.

L. Kranthi Kumar and P. J. A. Alphonse, “COVID-19 Disease Diagnosis with Light-Weight CNN Using Modified MFCC and Enhanced GFCC from Human Respiratory Sounds,” Eur. Phys. J. Spec. Top., pp. 1–18, 2022.

T. Ai et al., “Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases.,” Radiology, vol. 296, no. 2, pp. E32–E40, 2020.

V. Puspita et al., “Optimization of Digital Marketing Integration Through The Role of The Millenial Generation as MSMEs Effort To Survive During The Covid-19 Pandemic,” in 2021 9th Int. Conf. Cyber IT Serv. Manag. (CITSM), 2021, pp. 1–5.

B. H. Hayadi, J. Kim, K. Hulliyah, and H. T. Sukmana, “Predicting Airline Passenger Satisfaction with Classification Algorithms,” Int. J. Inform. Inf. Syst., vol. 4, no. 1, pp. 82–94, 2021.

M. Chaniago, A. A. Sugiharto, Q. D. Khatulistiwa, Z. Sari, and A. Eko, “Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm,” J. Rekayasa Sist. dan Teknol. Inf., vol. 5, no. 158, pp. 190–197, 2022.

R. Anand, V. Sowmya, Vijaykrishnamenon, E. A. Gopalakrishnan, and K. P. Soman, “Modified Vgg Deep Learning Architecture For Covid-19 Classification Using Bio-Medical Images,” in IOP Conf. Ser.: Mater. Sci. Eng., 2021, vol. 1084, no. 1, p. 12001.

T. Wang, Y. Zhao, L. Zhu, G. Liu, Z. Ma, and J. Zheng, “Lung CT Image Aided Detection COVID-19 based on Alexnet Network,” in 2020 5th Int. Conf. Commun. Image Signal Process. (CCISP), 2020, pp. 199–203.

E. Soares, P. Angelov, S. Biaso, M. H. Froes, and K. Abe, “SARS-CoV-2 CT-scan Dataset : A Large Dataset of Real Patients CT Scans for SARS-CoV-2 Identification,” medRxiv, pp. 1–8, 2020.

M. B. Patel, S. M. Parikh, and A. R. Patel, “Global normalization for fingerprint image enhancement,” in Int. Conf. Comput. Vis. Bio Inspired Comput.g, 2020, pp. 1059–1066.

L. Huang, J. Qin, Y. Zhou, F. Zhu, L. Liu, and L. Shao, “Normalization Techniques in Training DNNs : Methodology , Analysis and Application,” arXiv, pp. 1–20, 2020.

R. C. Gonzales and R. E. Woods, Digital Image Processing, Third Edit. Prentice Hall, 2017.

A. K. Nugroho, I. Permadi, and M. Faturrahim, “Improvement of Image Quality Using Convolutional Neural Networks Method,” Sci. J. Informatics, vol. 9, no. 1, pp. 95–103, 2022.

D. R. Hartawan, T. W. Purboyo, and C. Setianingsih, “Disaster Victims Detection System Using Convolutional Neural Network (CNN) Method,” in 2019 IEEE Int. Conf. Ind. 4.0 Artif. Intell. Commun. Technol. (IAICT), 2019, pp. 105–111.

T. Purwaningsih, I. A. Anjani, and P. B. Utami, “Convolutional Neural Networks Implementation for Chili Classification,” in 2018 Int. Symp. Adv. Intell. Inform. (SAIN), 2018, pp. 190–194.

A. Saxena, S. P. Singh, and V. H. Gaidhane, “A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks,” arXiv, 2022.

M. Lin, Q. Chen, and S. Yan, “Network In Network,” arXiv, 2013.

N. F. Nissa, A. Janiati, N. Cahya, and P. Astuti, “Application of Deep Learning Using Convolutional Neural Network ( CNN ) Method for Women ’ s Skin Classification,” Sci. J. Informatics, vol. 8, no. 1, pp. 144–153, 2021.

A. Chattopadhyay and M. Maitra, “MRI-based Brain Tumour Image Detection Using CNN Based Deep Learning Method,” Neurosci. Informatics, vol. 2, no. 4, p. 100060, 2022.

N. Saffari et al., “Fully Automated Breast Density Segmentation and Classification Using Deep Learning.,” Diagnostics (Basel, Switzerland), vol. 10, no. 11, Nov. 2020.

Y. N. Fu’adah, N. K. C. Pratiwi, M. A. Pramudito, and N. Ibrahim, “Convolutional Neural Network (CNN) for Automatic Skin Cancer Classification System,” IOP Conf. Ser. Mater. Sci. Eng., vol. 982, no. 1, 2020, p. 12005.

J. D. Dormer, M. Halicek, L. Ma, C. M. Reilly, E. Schreibmann, and B. Fei, “Convolutional Neural Networks for the Detection of Diseased Hearts Using CT Images and Left Atrium Patches.,” in Proc. SPIE--Int. Soc. Opt. Eng., 2018, vol. 10575.

B. Korbar et al., “Deep Learning for Classification of Colorectal Polyps on Whole-slide Images,” J. Pathol. Inform., vol. 8, no. 1, p. 30, 2017.

T. L. Nikmah, M. Z. Ammar, Y. R. Allatif, R. M. P. Husna, P. A. Kurniasari, and A. S. Bahri, “Comparison of LSTM , SVM , and Naive Bayes for Classifying Sexual Harassment Tweets,” J. Soft Comput. Explor., vol. 3dend, no. 2, pp. 131–137, 2022.

A. Çı, M. Yıldırı, and Y. Eroğlu, “Traitement du Signal Classification of Pneumonia Cell Images Using Improved ResNet50 Model,” Int. Inf. Eng. Technol. Assoc. (IIETA), vol. 38, no. 1, pp. 165–

| Scientific Journal of Informatics, Vol. 9, No. 2, Nov 2022

, 2021.

C. A. Sari, W. Shinta, and H. Rahmalan, “A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification,” Sci. J. Informatics, vol. 8, no. 1, pp. 76–83, 2021.

Citation:

K. L. Kohsasih, B. Herawan Hayadi, “Classification SARS-CoV-2 Disease based on CT-Scan Image Using Convolutional Neural Network”, Scientific Journal of Informatics, vol. 09, no. 2, pp. 197-204, Nov. 2022.

Publication:
Vol 9 No 2 (2022): Scientific Journal of Informatics
DOI:
10.15294/sji.v9i2.36583

Copyright:

Copyright (c) Scientific Journal of Informatics
Klasifikasi Virus SARS-CoV-2 Berdasarkan Citra CT-Scan Menggunakan Convolutional Neural Network
kelvin leonardi 1 Desember 2022
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