Abstract
The use of convolutional neural networks in Covid classification has a positive impact on the speed of justification and can provide high accuracy. But on the one hand, the many parameters on CNN will also have an impact on the resulting accuracy. CNN requires time and a heavy level of computation. Setting the right parameters will provide high accuracy. This study examines the performance of CNN with variations in image size and minibatch. Parameter settings used are max epoch values of 100, minibatch variations of 32, 64, and 128, and learning rate of 0.1 with image size inputs of 50,100, and 150 variations on the level of accuracy. The dataset consists of training data and test data, 200 images, which are divided into two categories of normal and abnormal images (Covid). The results showed an accuracy with the use of minibatch 128 with the highest level of accuracy at image size 150 × 150 on test data of 99,08%. The size of the input matrix does not always have an impact on increasing the level of accuracy, especially on the minibatch 32. The parameter setting on CNN was dependent on the CNN architecture, the dataset used, and the size of the dataset. One can imply that optimization parameter in CNN can approve good accuration.
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References
Satuan Tugas Penanganan COVID-19: Perkembangan Kasus Terkonfirmasi Positif Covid-19. https://covid19.go.id/. Last accessed 11 May 2022
Wordometers. Coronavirus death toll and trends—worldomet. https://www.worldometers.info/coronavirus/coronavirus-death-toll/. Last accessed 17 September 2022
He JL, Luo L, Luo ZD, Lyu JX, Ng MY, Shen XP, Wen Z (2020) Diagnostic performance between CT and initial real-time RT-PCR for clinically suspected 2019 coronavirus disease (COVID-19) patients outside Wuhan, China. Respir Med 168. https://doi.org/10.1016/j.rmed.2020.105980
Hassantabar S, Ahmadi M, Sharifi A (2020) Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches. Chaos Solitons Fractals 140:110170. https://doi.org/10.1016/j.chaos.2020.110170
Irmak E (2021) COVID-19 disease severity assessment using CNN model. IET Image Process 15:1814–1824. https://doi.org/10.1049/ipr2.12153
Singh KK, Siddhartha M, Singh A (2020) Diagnosis of coronavirus disease (COVID-19) from chest X-ray images using modified XceptionNet
Tafti A, Byerly DW (2022) X-ray radiographic patient positioning. StatPearls Publishing LLC
Cho J, Lee K, Shin E, Choy G, Do S (2016) How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? ICLR
Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, Raymond Geis J, Pandharipande PV, Brink JA, Dreye KJ (2018) Current applications and future impact of machine learning in radiology. RSNA 2:318–328. https://doi.org/10.1148/radiol.2018171820
Nigam B, Nigam A, Jain R, Dodia S, Arora N, Annappa B (2021) COVID-19: automatic detection from X-ray images by utilizing deep learning methods. Expert Syst Appl 176. https://doi.org/10.1016/j.eswa.2021.114883
Tahir AM, Chowdhury MEH, Khandakar A, Rahman T, Qiblawey Y, Khurshid U, Kiranyaz S, Ibtehaz N, Rahman MS, Al-Maadeed S, Mahmud S, Ezeddin M, Hameed K, Hamid T (2021) COVID-19 infection localization and severity grading from chest X-ray images. Comput Biol Med 139. https://doi.org/10.1016/j.compbiomed.2021.105002
Padmakala S, Revathy S, Vijayalakshmi K, Mathankumar M (2022) CNN supported automated recognition of Covid-19 infection in chest X-ray images. Mater Today Proc. https://doi.org/10.1016/j.matpr.2022.05.003
Jain G, Mittal D, Thakur D, Mittal MK (2020) A deep learning approach to detect Covid-19 coronavirus with X-Ray images. Biocybern Biomed Eng. 40:1391–1405. https://doi.org/10.1016/j.bbe.2020.08.008
Sekeroglu B, Ozsahin I (2020) Detection of COVID-19 from chest X-ray images using convolutional neural networks. SLAS Technol 25:553–565. https://doi.org/10.1177/2472630320958376
Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS (2021) Pneumonia classification using deep learning from chest X-ray images during COVID-19. Cognit Comput. https://doi.org/10.1007/S12559-020-09787-5
Hertel R, Benlamri R (2022) A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis. Biomed Eng Adv 100041. https://doi.org/10.1016/j.bea.2022.100041
Shambhu S, Koundal D, Das P, Sharma C (2022) Binary classification of COVID-19 CT images using CNN: COVID diagnosis using CT. Int J E-Health Med Commun 13. https://doi.org/10.4018/IJEHMC.20220701.oa4
Ahmed WS, Karim A, Amir A (2020) The impact of filter size and number of filters on classification accuracy in CNN. In: 2020 international conference on computer science and software engineering (CSASE). IEEE, pp 88–93. https://doi.org/10.1109/CSASE48920.2020.9142089
Mishra VK, Kumar S, Shukla N (2017) Image acquisition and techniques to perform image acquisition. SAMRIDDHI: A J Phys Sci Eng Technol 9:21–24. https://doi.org/10.18090/samriddhi.v9i01.8333
Bushong S (2012) Radiologic science for technologists. Elsevier Mosby, St. Louis
Thirukrishna JT, Reddy S, Krishna S, Shashank P, Srikanth S, Raghu V, Srikanth S, Raghu V (2022) Survey on diagnosing CORONA VIRUS from radiography chest X-ray images using convolutional neural networks 124:2261–2270. https://doi.org/10.1007/s11277-022-09463-x
Philips MB, Luke JJ, Joseph R, Balaji M (2019) Impact of image size on accuracy and generalization of convolutional neural networks. IJRAR19SP012 Int J Res Anal Rev
Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A (2020) Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. https://doi.org/10.2214/AJR.20.23034
American College of Radiology (2017) ACR–SPR–STR: practise parameter chest radiography
Colman J, Zamfir G, Sheehan F, Berrill M, Saikia S, Saltissi F (2021) Chest radiograph characteristics in COVID-19 infection and their association with survival. Eur J Radiol Open. 8:100360. https://doi.org/10.1016/j.ejro.2021.100360
Uyar K, Taşdemir Ş, Özkan Ilker A (2021) The analysis and optimization of CNN hyperparameters with fuzzy tree model for image classification. Turk J Electr Eng Comput Sci. https://doi.org/10.3906/elk-2107-130
Thambawita V, Strümke I, Hicks SA, Halvorsen P, Parasa S, Riegler MA (2021) Impact of image resolution on deep learning performance in endoscopy image classification: an experimental study using a large dataset of endoscopic images. Diagnostics 11. https://doi.org/10.3390/diagnostics11122183
Rukundo O (2022) Effects of image size on deep learning. https://doi.org/10.48550/arXiv.2101.11508
Rochmawati N, Hidayati HB, Yamasari Y, Tjahyaningtijas HPA, Yustati W, Prihanto A (2021) Analysis of learning rate and batch size on covid classification using deep learning with Adam’s optimizer. J Inf Eng Educ Technol 5:44–48
Luo C, Li X, Wang L, He J, Li D, Zhou J (2018) How does the data set affect CNN-based image classification performance? In: 2018 5th international conference on systems and informatics (ICSAI). IEEE, pp 361–366 (2018). https://doi.org/10.1109/ICSAI.2018.8599448
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Rochmayanti, D. et al. (2023). Accuration of Classification of Covid with Convolutional Neural Network-Based Image Chest X-ray with Variations in Image Size and Batch Size. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 1008. Springer, Singapore. https://doi.org/10.1007/978-981-99-0248-4_13
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