QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

G.Naga  Chandrika; J.  Karpagam; Titus Richard; Finney Daniel  Shadrach; T Triwiyanto

doi:10.35882/jeeemi.v6i2.391

G.Naga Chandrika Department of Information Technology, VNR VJIET, Hyderabad, 500090 Telangana, India https://orcid.org/0000-0002-8991-5930
J. Karpagam Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Coimbatore, India https://orcid.org/0000-0002-0867-1429
Titus Richard Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India https://orcid.org/0000-0001-9026-367X
Finney Daniel Shadrach Department of ECE, KPR Institute of Engineering and Technology, Coimbatore, India https://orcid.org/0000-0003-1037-0614
T Triwiyanto Department of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia https://orcid.org/0000-0003-3179-8900

DOI: https://doi.org/10.35882/jeeemi.v6i2.391

Keywords: Machine Learning (ML), COVID-19 Diagnosis, Discrete Wavelet Transform (DWT), Computed Tomography (CT) Images, Classification

Abstract

The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.

Downloads

Download data is not yet available.

References

F. Wu, et al., A new coronavirus associated with human respiratory disease in China, Nature 579 (7798) (2020) 265–269, http://dx.doi.org/ 10.1038/s41586-020-2008-3.

Coronavirus disease (COVID-19) pandemic. https://www.who.int/ emergencies/diseases/novel-coronavirus-2019.

N. Subramanian, O. Elharrouss, S. Al-Maadeed, M. Chowdhury, A review of deep learning-based detection methods for COVID-19, Comput. Biol. Med.143 (2022) 105233, http://dx.doi.org/10.1016/j.compbiomed.2022.105233.

G.D. Rubin, et al., The role of chest imaging in patient management during the COVID-19 pandemic, Chest 158 (1) (2020) 106–116, http: //dx.doi.org/10.1016/j.chest.2020.04.003.

R. Singh, et al., Corona virus (COVID-19) symptoms prevention and treatment: A short review, J. Drug Deliv. Ther. 11 (2-S) (2021) 118–120, http://dx.doi.org/10.22270/jddt.v11i2-S.4644.

S. R, et al., An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction, Sustain. Cities Soc. (2022) 103713, http://dx.doi.org/10.1016/j.scs.2022.103713.

A. Heidari, N. Jafari Navimipour, M. Unal, S. Toumaj, The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions, Comput. Biol. Med. 141 (2021) (2022) 105141, http://dx.doi.org/10.1016/j.compbiomed.2021.105141.

J.M. Sharfstein, S.J. Becker, M.M. Mello, Diagnostic testing for the novel coronavirus, JAMA 323 (15) (2020) 1437, http://dx.doi.org/10.1001/jama.2020.3864.

S. Stephanie, et al., Determinants of chest radiography sensitivity for COVID-19: A multi-institutional study in the United States, Radiol. Cardiothorac. Imaging 2 (5) (2020) e200337, http://dx.doi.org/10.1148/ryct. 2020200337.

R. Liu, et al., Clinica Chimica Acta positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to 2020, Clin. Chim. Acta 505 (March) (2020) 172–175, http://dx.doi.org/10.1016/j.cca.2020.03.009.

M. Dramé, et al., Should RT-PCR be considered a gold standard in the diagnosis of COVID-19? J. Med. Virol. 92 (11) (2020) 2312–2313, http: //dx.doi.org/10.1002/jmv.25996.

J. Xie, et al., Characteristics of patients with coronavirus disease (COVID-19) confirmed using an IgM-IgG antibody test, J. Med. Virol. 92 (10) (2020) 2004–2010, http://dx.doi.org/10.1002/jmv.25930.

H. Hassan, et al., Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review, Comput. Methods Programs Biomed. 218 (2022) 106731, http://dx.doi.org/10.1016/j.cmpb. 2022.106731.

P. Gaur, V. Malaviya, A. Gupta, G. Bhatia, R.B. Pachori, D. Sharma, COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning, Biomed. Signal Process. Control 71 (PA) (2022) 103076, http://dx.doi.org/10.1016/j.bspc.2021.103076.

[14] Suganyadevi, S., Seethalakshmi, V. Deep recurrent learning based qualified sequence segment analytical model (QS2AM) for infectious disease detection using CT images. Evolving Systems (2023). https://doi.org/10.1007/s12530-023-09554-5

F. Ucar, D. Korkmaz, COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease, 2019 (COVID-19) from X-ray images, Med. Hypotheses 140 (April) (2020) 109761, http://dx.doi.org/10.1016/j. mehy.2020.109761.

E. Başaran, Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method, Signal, Image Video Process (2022) http://dx.doi.org/10.1007/s11760-022-02141-2.

G. Çelik, M.F. Talu, A new 3D MRI segmentation method based on generative adversarial network and atrous convolution, Biomed. Signal Process. Control 71 (PA) (2022) 103155, http://dx.doi.org/10.1016/j.bspc. 2021.103155.

Suganyadevi, S., Seethalakshmi, V. & Balasamy, K. A review on deep learning in medical image analysis. Int J Multimed Info Retr (2021). https://doi.org/10.1007/s13735-021-00218-1

I. Goodfellow, et al., Generative adversarial networks, Commun. ACM 63 (11) (2020) 139–144, http://dx.doi.org/10.1145/3422622.

G. Çelik, M.F. Talu, Generating the image viewed from EEG signals, Pamukkale Univ. J. Eng. Sci. 27 (2) (2021) 129–138, http://dx.doi.org/10.5505/pajes.2020.76399.

A. Esteva, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature 542 (7639) (2017) 115–118, http://dx.doi.org/10.1038/nature21056.

J.H. Tan, et al., Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network, Inf. Sci. (Ny) 420 (2017) 66–76, http://dx.doi.org/10.1016/j.ins.2017.08.050.

E. Başaran, Z. Cömert, Y. Çelik, Neighbourhood component analysis and deep feature-based diagnosis model for middle ear otoscope images, Neural Comput. Appl. (2022) http://dx.doi.org/10.1007/s00521-021-06810-0.

Y. Celik, M. Talo, O. Yildirim, M. Karabatak, U.R. Acharya, Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images, Pattern Recognit. Lett. 133 (2020) 232–239, http://dx.doi.org/10.1016/j.patrec.2020.03.011.

Bozdag, F.M. Talu, Pyramidal nonlocal network for histopathological image of breast lymph node segmentation, Int. J. Comput. Intell. Syst. 14 (1) (2021) 122–131, http://dx.doi.org/10.2991/ijcis.d.201030.001.

M. Talo, O. Yildirim, U.B. Baloglu, G. Aydin, U.R. Acharya, Convolutional neural networks for multi-class brain disease detection using MRI images, Comput. Med. Imaging Graph. 78 (2019) 101673, http://dx.doi.org/10.1016/j.compmedimag.2019.101673.

Suganyadevi, S., Seethalakshmi, V. CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network. Wireless Pers Commun (2022). https://doi.org/10.1007/s11277-022-09864-y

G. Gaál, B. Maga, A. Lukács, Attention U-net based adversarial architectures for chest X-ray lung segmentation, CEUR Workshop Proc. 2692 (2020) 1–7.

J.C. Souza, J.O. Bandeira Diniz, J.L. Ferreira, G.L. França da Silva, A. Corrêa Silva, A.C. de Paiva, An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks, Comput. Methods Programs Biomed. 177 (2019) 285–296, http://dx.doi.org/10.1016/j.cmpb.2019.06.005.

Ö. Yıldırım, P. Pławiak, R.S. Tan, U.R. Acharya, Arrhythmia detection using deep convolutional neural network with long duration ECG signals, Comput. Biol. Med. 102 (September) (2018), 411–420, http://dx.doi.org/10.1016/j.compbiomed.2018.09.009.