Perbandingan Arsitektur Convolutional Neural Network Pada Klasifikasi Pneumonia, COVID-19, Lung Opacity, dan Normal Menggunakan Citra Sinar-X Thoraks
DOI:
https://doi.org/10.25126/jtiik.2022976742Abstrak
Covid-19 telah mewabah sejak awal Tahun 2020. Meskipun terjadi penurunan jumlah kasus penderita Covid-19, namun masih terdapat beberapa kasus baru karena terjadi mutasi virus. Selain Covid-19, prevalensi pneumonia juga masih tinggi. Oleh karena itu, perlu dilakukan klasifikasi Covid-19 dengan pneumonia meskipun pascapandemi. Salah satu cara yang digunakan untuk mendeteksi Covid-19 dan pneumonia adalah menggunakan citra sinar-X dada. Pada studi ini dilakukan tidak hanya Covid-19 dan pneumonia, tetapi juga lung opacity dan normal. Beberapa tahun terakhir, marak digunakan pendekatan klasifikasi berbasis kecerdasan buatan. Beberapa studi telah dilakukan dengan menggunakan pendekatan deep learning berbasis arsitektur CNN. Pada studi ini, klasifikasi keempat kelas di atas dilakukan dengan menggunakan data yang lebih banyak, yaitu 21.165 citra sinar-X dada. Selain itu, dilakukan perbandingan kinerja sembilan arsitektur CNN, yaitu Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2, dan ResNet152v2. Sebagai tambahan, studi ini juga membandingkan kinerja dua pengoptimasi, yaitu Adam dan SGD untuk masing-masing arsitektur CNN. Kinerja tertinggi diperoleh dengan menggunakan arsitektur CNN berbasis ResNet50v1 dan pengoptimasi Adam dengan nilai rerata akurasi pelatihan, validasi, dan pengujian mencapai 92,22 ± 0,25 %.
Abstract
Covid-19 has been a global pandemic since the beginning of 2020. Although there has been a decrease in the number of cases of Covid-19, however, there are still some new cases due to virus mutation. Besides Covid-19, the prevalence of pneumonia is still high. Therefore, it is necessary to classify Covid-19 and pneumonia not only during a pandemic but also post-pandemic. One of the methods is using chest X-ray images. In this study, not only Covid-19 and pneumonia but also lung opacity and normal were carried out. In recent years, artificial intelligence-based classification approaches have been widely used. Several studies have been conducted using a deep learning approach based on Convolutional Neural Networks (CNN) architecture. This study aims to classify the four classes using 21,165 chest X-ray images. In addition, a comparison of the performance of nine CNN architectures was perfomed, i.e. Inception-ResNet, DenseNet201, InceptionV3, ResNet50v1, ResNet101, ResNet152, ResNet50v2, ResNet101v2 and ResNet152v2. In addition, this study also compares the performance of two optimizers, i.e. Adam and Stochastic Gradient Descent (SGD) for each CNN architecture. The highest performance was obtained using ResNet50v1 and Adam optimizer with the average value of training, validation, and testing accuracy of 92.22 ± 0.25 %.
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AKL, E.A., BLAZIC, I., YAACOUB, S., FRIJA, G., CHOU, R., APPIAH, J.A., FATEHI, M., FLOR, N., HITTI, E., JAFRI, H. dan JIN, Z.Y., 2021. Use of chest imaging in the diagnosis and management of COVID-19: a WHO rapid advice guide. Radiology.
AKTER, S., SHAMRAT, F.M.J.M., CHAKRABORTY, S., KARIM, A. dan AZAM, S., 2021. COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images. Biology, [online] 10(11), p.1174. https://doi.org/10.3390/biology10111174.
ALAM, N.-A.-A., AHSAN, M., BASED, MD.A., HAIDER, J. dan KOWALSKI, M., 2021. COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning. Sensors, [online] 21(4), p.1480. https://doi.org/10.3390/s21041480.
APOSTOLOPOULOS, I.D. dan MPESIANA, T.A., 2020. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine, 43(2), pp.635-640.
BAI, H.X., HSIEH, B., XIONG, Z., HALSEY, K., CHOI, J.W., TRAN, T.M.L., PAN, I., SHI, L.B., WANG, D.C., MEI, J. dan JIANG, X.L., 2020. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology.
BHATTACHARYYA, A., BHAIK, D., KUMAR, S., THAKUR, P., SHARMA, R. dan PACHORI, R.B., 2022. A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images. Biomedical Signal Processing and Control, 71, p.103182.
CHOWDHURY, M.E., RAHMAN, T., KHANDAKAR, A., MAZHAR, R., KADIR, M.A., MAHBUB, Z.B., ISLAM, K.R., KHAN, M.S., IQBAL, A., AL EMADI, N. dan REAZ, M.B.I., 2020. Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access, 8, pp.132665-132676.
FAROOQ, M. dan HAFEEZ, A., 2020. Covid-resnet: A deep learning framework for screening of covid19 from radiographs. arXiv preprint arXiv:2003.14395.
GILLMAN, A.G., LUNARDO, F., PRINABLE, J., BELOUS, G., NICOLSON, A., MIN, H., TERHORST, A. dan DOWLING, J.A., 2021. Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review. Physical and engineering sciences in medicine, pp.1-17.
HAGHANIFAR, A., MAJDABADI, M.M., CHOI, Y., DEIVALAKSHMI, S. dan KO, S., 2022. Covid-cxnet: Detecting covid-19 in frontal chest x-ray images using deep learning. Multimedia Tools and Applications, pp.1-31.
HEMDAN, E.E.D., SHOUMAN, M.A. dan KARAR, M.E., 2020. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055.
HOU, J. dan GAO, T., 2021. Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection. Scientific Reports, 11(1), pp.1-15.
HUSSAIN, E., HASAN, M., RAHMAN, M.A., LEE, I., TAMANNA, T. dan PARVEZ, M.Z., 2021. CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos, Solitons & Fractals, 142, p.110495.
IBRAHIM, A.U., OZSOZ, M., SERTE, S., AL-TURJMAN, F. dan YAKOI, P.S., 2021. Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognitive Computation, pp.1-13.
ISLAM, M.M., KARRAY, F., ALHAJJ, R. dan ZENG, J., 2021. A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19). Ieee Access, 9, pp.30551-30572.
ISMAEL, A.M. dan ŞENGÜR, A., 2021. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications, 164, p.114054.
JAIN, R., GUPTA, M., TANEJA, S. dan HEMANTH, D.J., 2021. Deep learning-based detection and analysis of COVID-19 on chest X-ray images. Applied Intelligence, 51(3), pp.1690-1700.
KARAKANIS, S. dan LEONTIDIS, G., 2021. Lightweight deep learning models for detecting COVID-19 from chest X-ray images. Computers in biology and medicine, 130, p.104181.
KHAN, A.I., SHAH, J.L. dan BHAT, M.M., 2020. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer methods and programs in biomedicine, 196, p.105581.
KHAN, M., MEHRAN, M.T., HAQ, Z.U., ULLAH, Z., NAQVI, S.R., IHSAN, M. dan ABBASS, H., 2021. Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review. Expert systems with applications, 185, p.115695.
KHAN, S.H., SOHAIL, A., KHAN, A. dan LEE, Y.S., 2022. COVID-19 detection in chest X-ray images using a new channel boosted CNN. Diagnostics, 12(2), p.267.
KINGMA, D.P. dan BA, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
LOEY, M., EL-SAPPAGH, S. dan MIRJALILI, S., 2022. Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data. Computers in Biology and Medicine, 142, p.105213.
LUZ, E., SILVA, P., SILVA, R., SILVA, L., GUIMARÃES, J., MIOZZO, G., MOREIRA, G. dan MENOTTI, D., 2022. Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Research on Biomedical Engineering, 38(1), pp.149-162.
MADAAN, V., ROY, A., GUPTA, C., AGRAWAL, P., SHARMA, A., BOLOGA, C. dan PRODAN, R., 2021. XCOVNet: chest X-ray image classification for COVID-19 early detection using convolutional neural networks. New Generation Computing, 39(3), pp.583-597.
MOUSAVI, Z., SHAHINI, N., SHEYKHIVAND, S., MOJTAHEDI, S. dan ARSHADI, A., 2022. COVID-19 detection using chest X-ray images based on a developed deep neural network. SLAS technology, 27(1), pp.63-75.
NARIN, A., KAYA, C. dan PAMUK, Z., 2021. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24(3), pp.1207-1220.
NAYAK, S.R., NAYAK, D.R., SINHA, U., ARORA, V. dan PACHORI, R.B., 2021. Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomedical Signal Processing and Control, 64, p.102365.
OZTURK, T., TALO, M., YILDIRIM, E.A., BALOGLU, U.B., YILDIRIM, O. dan ACHARYA, U.R., 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in biology and medicine, 121, p.103792.
PHAM, T.D., 2021. Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?. Health Information Science and Systems, 9(1), pp.1-11.
RAHMAN, A., MUNIR, S.M., YOVI, I. dan MAKMUR, A., 2021. The Relationship of Chest X-Ray in COVID-19 Patients and Disease Severity in Arifin Achmad General Hospital Riau. Jurnal Respirasi, 7(3), pp.114-121.
RAHMAN, T., KHANDAKAR, A., QIBLAWEY, Y., TAHIR, A., KIRANYAZ, S., KASHEM, S.B.A., ISLAM, M.T., MAADEED, S.A., ZUGHAIER, S.M., KHAN, M.S. dan CHOWDHURY, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. arXiv preprint arXiv:2012.02238.
ROBERTS, M., DRIGGS, D., THORPE, M., GILBEY, J., YEUNG, M., URSPRUNG, S., AVILES-RIVERO, A.I., ETMANN, C., MCCAGUE, C., BEER, L. dan Weir-MCCALL, J.R., 2021. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nature Machine Intelligence, 3(3), pp.199-217.
SANTA CRUZ, B.G., BOSSA, M.N., SÖLTER, J. dan HUSCH, A.D., 2021. Public covid-19 x-ray datasets and their impact on model bias–a systematic review of a significant problem. Medical image analysis, 74, p.102225.
SETHY, P.K. dan BEHERA, S.K., 2020. Detection of coronavirus disease (covid-19) based on deep features.
SHI, H., HAN, X., JIANG, N., CAO, Y., ALWALID, O., GU, J., FAN, Y. dan ZHENG, C., 2020. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. The Lancet infectious diseases, 20(4), pp.425-434.
SITAULA, C. dan HOSSAIN, M.B., 2021. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Applied Intelligence, 51(5), pp.2850-2863.
TANG, S., WANG, C., NIE, J., KUMAR, N., ZHANG, Y., XIONG, Z. dan BARNAWI, A., 2021. EDL-COVID: Ensemble deep learning for COVID-19 case detection from chest X-ray images. IEEE Transactions on Industrial Informatics, 17(9), pp.6539-6549.
TOĞAÇAR, M., ERGEN, B. dan CÖMERT, Z., 2020. COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches. Computers in biology and medicine, 121, p.103805.
UCAR, F. dan KORKMAZ, D., 2020. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical hypotheses, 140, p.109761.
WANG, L., LIN, Z.Q. dan WONG, A., 2020. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), pp.1-12.
WANG, W., Li, Y., Li, J., ZHANG, P. dan WANG, X., 2021. Detecting COVID-19 in Chest X-Ray Images via MCFF-Net. Computational Intelligence and Neuroscience, 2021.
WORLD HEALTH ORGANIZATION. WHO Director-General's opening remarks at the media briefing on COVID-19 - 11 March 2020 2020 [Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020.
WORLD HEALTH ORGANIZATION, 2020. Use of chest imaging in COVID-19: a rapid advice guide, 11 June 2020 (No. WHO/2019-nCoV/Clinical/Radiology_imaging/2020.1). World Health Organization.
YASIN, R. dan GOUDA, W., 2020. Chest X-ray findings monitoring COVID-19 disease course and severity. Egyptian Journal of Radiology and Nuclear Medicine, 51(1), pp.1-18.
ZHOU, C., SONG, J., ZHOU, S., ZHANG, Z. dan XING, J., 2021. COVID-19 detection based on image regrouping and ResNet-SVM using chest X-ray images. Ieee Access, 9, pp.81902-81912.
ZU, Z.Y., DI JIANG, M., XU, P.P., CHEN, W., NI, Q.Q., LU, G.M. dan ZHANG, L.J., 2020. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology.
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