MobileNet Classifier for Detecting Chest X-Ray Images of COVID-19 based on Convolutional Neural Network


ST. Aminah Dinayati Ghani(1); Indo Intan(2*); Muhammad Rizal(3);

(1) Universitas Dipa Makassar
(2) Universitas Dipa Makassar
(3) Universitas Dipa Makassar
(*) Corresponding Author

  

Abstract


Since the COVID-19 pandemic occurred all over the world, numerous studies were carried out to overcome this problem, including COVID-19 image analysis. An expert analysis based on the Chest X-ray images of COVID-19 determines the progression of the lung condition. Eye visualization and expertise of a radiologist have limitations in handling big cases. This study aims to implement the Convolutional Neural Network (CNN) and MobileNet models as deep learning models to classify chest X-ray images into multiclassification, three categories: COVID-19, normal, and virus. The processes were pre-processing and processing. The pre-processing stage was preparing data, and the processing stage was the implementation model and investigating the best model performance in both convolution and classification in depth-wise convolution and batch normalization. The metrics were accuracy, precision, f1-score, and recall. The CNN results of accuracy, precision, recall, and f1-score respectively were 0.94; 0.99; 0.95; and 0.96. The MobileNet results of the metrics were 0.97; 0.98; 0.99, and 0.99. The MobileNet outperforms the CNN results due to depth-wise convolution and batch normalization. Both models contribute to the faster epoch of the best hyperparameter to achieve loss and accuracy convergence. The models are worth recommending to deployment front-end.


Keywords


Chest X-Ray; CNN; COVID-19; Image Analysis; MobileNet

  
  

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doi  https://doi.org/10.33096/ilkom.v15i3.1780.488-497
  

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References


A. P. Agustin, A. C. Fauzan, and Harliana, “Implementasi K-Nearest Neighbor Dengan Jarak Minkowski Untuk Deteksi Dini Covid-19 Pada Citra Ct-Scan Paru - Paru,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 4, no. 1, pp. 23–30, 2022.

Q. Li et al., “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia,”

N. Engl. J. Med., vol. 382, no. 13, pp. 1199–1207, 2020, doi: 10.1056/nejmoa2001316.

Y. M. Arabi, S. Murthy, and S. Webb, “COVID-19: a novel coronavirus and a novel challenge for critical care,” Intensive Care Med., vol. 46, no. 5, pp. 833–836, 2020, doi: 10.1007/s00134-020-05955-1.

M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1207–1216, 2016, doi: 10.1109/TMI.2016.2535865.

Y. Wang et al., “Precise pulmonary scanning and reducing medical radiation exposure by developing a clinically applicable intelligent CT system: Toward improving patient care,” EBioMedicine, vol. 54, 2020, doi: 10.1016/j.ebiom.2020.102724.

K. Buys, C. Cagniart, A. Baksheev, T. De Laet, J. De Schutter, and C. Pantofaru, “An adaptable system for RGB-D based human body detection and pose estimation,” J. Vis. Commun. Image Represent., vol. 25, no. 1, pp. 39–52, 2014, doi: 10.1016/j.jvcir.2013.03.011.

D. Selvaraj, A. Venkatesan, V. G. V. Mahesh, and A. N. Joseph Raj, “An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images,” Int. J. Imaging Syst. Technol., vol. 31, no. 1, pp. 28–46, 2021, doi: 10.1002/ima.22525.

G. Jia, H. Lam, and Y. Xu, “Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information ,” no. January, 2020.

B. Abraham and M. S. Nair, “Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier,” Biocybern. Biomed. Eng., vol. 40, no. 4, pp. 1436–1445, 2020, doi: 10.1016/j.bbe.2020.08.005.

R. Karthik, R. Menaka, and M. Hariharan, “Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN,” Appl. Soft Comput., vol. 99, p. 106744, 2021, doi: 10.1016/j.asoc.2020.106744.

A. Rehman, T. Sadad, T. Saba, A. Hussain, and U. Tariq, “Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning,” IT Prof., vol. 23, no. 4, pp. 57–62, 2021, doi: 10.1109/MITP.2020.3042379.

R. Shrestha and L. Shrestha, “Coronavirus disease 2019 (Covid-19): A pediatric perspective,” J. Nepal Med. Assoc., vol. 58, no. 227, pp. 525–532, 2020, doi: 10.31729/jnma.4977.

A. Abbas, M. M. Abdelsamea, and M. M. Gaber, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network,” Appl. Intell., vol. 51, no. 2, pp. 854–864, 2021, doi: 10.1007/s10489-020-01829-7.

H. Shi et al., “Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study,” Lancet Infect. Dis., vol. 20, no. 4, pp. 425–434, 2020, doi: 10.1016/S1473- 3099(20)30086-4.

B. Yanti and U. Hayatun, “Peran pemeriksaan radiologis pada diagnosis Coronavirus disease 2019,” J. Kedokt. Syiah Kuala, vol. 20, no. 1, pp. 53–57, 2020, doi: 10.24815/jks.v20i1.18300.

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, Nov. 2020, doi: 10.1016/J.CHAOS.2020.110170.

S. Pathan, P. C. Siddalingaswamy, and T. Ali, “Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture,” Appl. Soft Comput., vol. 104, p. 107238, 2021, doi: 10.1016/j.asoc.2021.107238.

I. Sulistyowati and L. R. W. Utami, “Tingkat Kecemasan Radiografer dalam Memberikan Pelayanan Radiologi pada Masa Pandemi Covid-19 di Rumah Sakit Baitul Hikmah Kendal,” J. Ilmu dan Teknol. Kesehat. STIKES Widya Husada, vol. 12, no. 2, pp. 55–61, 2021.

S. Ahmad, “A Review of COVID-19 (Coronavirus Disease-2019) Diagnosis, Treatments and Prevention,”

Eurasian J. Med. Oncol., vol. 2019, 2020, doi: 10.14744/ejmo.2020.90853.

G. Wang, “A perspective on deep imaging,” IEEE Access, vol. 4, pp. 8914–8924, 2016, doi: 10.1109/ACCESS.2016.2624938.

E. Dandil, M. Cakiroglu, Z. Eksi, M. Ozkan, O. K. Kurt, and A. Canan, “Artificial neural network-based classification system for lung nodules on computed tomography scans,” 6th Int. Conf. Soft Comput. Pattern Recognition, SoCPaR 2014, pp. 382–386, 2014, doi: 10.1109/SOCPAR.2014.7008037.

J. Kuruvilla and K. Gunavathi, “Lung cancer classification using neural networks for CT images,” Comput. Methods Programs Biomed., vol. 113, no. 1, pp. 202–209, 2014, doi: 10.1016/j.cmpb.2013.10.011.

T. Manikandan and N. Bharathi, “Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier,” J. Med. Syst., vol. 40, no. 7, 2016, doi: 10.1007/s10916- 016-0539-9.

P. B. Sangamithraa and S. Govindaraju, “Lung tumour detection and classification using EK-Mean clustering,” Proc. 2016 IEEE Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2016, pp. 2201–2206, 2016, doi: 10.1109/WiSPNET.2016.7566533.

M. M. A. Asmaa Abbas, “Learning Trnasformations for Automated Classification of Manifestation of Tuberculosis using Convolutional Neural Network,” in 2018 13th International Conference on Computer Engineering and Systems (ICCES), 2018, pp. 122–126, doi: 10.1109/ICCES.2018.8639200.

W. Sun, B. Zheng, and W. Qian, “Computer aided lung cancer diagnosis with deep learning algorithms,”

Med. Imaging 2016 Comput. Diagnosis, vol. 9785, p. 97850Z, 2016, doi: 10.1117/12.2216307.

Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.

J. Wan et al., “Institutional Knowledge at Singapore Management University Deep learning for content- based image retrieval : A comprehensive study Chinese Academy of Sciences,” 2014.

L. Deng, “Deep Learning: Methods and Applications,” Found. Trends Signal Process., vol. 7, no. June 2014, pp. 197–387, 2014, doi: 10.1561/2000000039.

H. Greenspan, B. Van Ginneken, and R. M. Summers, “Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1153–1159, 2016, doi: 10.1109/TMI.2016.2553401.

I. D. Maysanjaya, “Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network ( Classification of Pneumonia Based on Lung X-rays Images using Convolutional Neural Network),” vol. 9, no. 2, pp. 190–195, 2020.

L. A. Andika, H. Pratiwi, and S. S. Handajani, “Lingga Aji Andika 1 , Hasih Pratiwi 2 , and Sri Sulistijowati Handajani 3 1,” pp. 331–340, 2019.

A. B. Handoko, I. K. Timotius, D. Utomo, F. Teknik, U. Kristen, and S. Wacana, “Klasifikasi Citra X-Ray COVID-19 Menggunakan Three-layered CNN Model,” no. April 2022, pp. 155–168.

N. A. Baghdadi, A. Malki, S. F. Abdelaliem, H. Magdy, M. Badawy, and M. Elhosseini, “An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network,” vol. 144, no. January, 2022.

A. G. Howard and W. Wang, “Applications,” 2012.

W. Wang, Y. Li, T. Zou, X. Wang, J. You, and Y. Luo, “A Novel Image Classification Approach via Dense- MobileNet Models,” vol. 2020, 2020.

S. Indolia, A. Kumar, S. P. Mishra, and P. Asopa, “ScienceDirect Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” Procedia Comput. Sci., vol. 132, pp. 679– 688, 2018, doi: 10.1016/j.procs.2018.05.069.

D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion matrix,” Comput. Oper. Res., vol. 152, no. December 2022, p. 106131, 2023, doi: 10.1016/j.cor.2022.106131.

K. Parang, L. Wiebe, and E. Knaus, Novel Approaches for Designing 5-O-Ester Prodrugs of 3-Azido-2,3- dideoxythymidine (AZT)., vol. 7, no. 10. 2012.


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