Acquiring the Ability to Identifying Covid19 using Deep CNN from Impulse Noise in Chest X-Ray Pictures
Keywords:
Covid19 Detection; Deep CCN; Image classification; Machine learning; impulse noiseAbstract
Utilizing CNNs, COVID19 is identified in X-ray pictures. Deep CNNs may have a harder time identifying things in noisy X-ray pictures. We provide a unique CNN technique that eliminates the need for preprocessing of noise in X-ray pictures by using adaptive convolution to enhance COVID19 detection. A CNN will therefore be more resistant to erratic noise. This method adds an adaptive convolution layer, an impulsive noise-map layer, and an adjustable scaling layer to the standard CNN architecture. Additionally, we employed a learning-to-augment technique with X-ray pictures that were noisy in order to enhance a deep CNN's generalization. The 2093 chest X-ray photos are divided into 1020 images showing a healthy image, 621 images showing pneumonia other than COVID-19, and 452 images showing COVID19. The architectures of pre-trained networks have been modified to increase their resilience to impulsive noise. Validation on noisy X-ray pictures showed that the proposed noise-robust layers and learning-to-augment strategy incorporated ResNet_50 led to 2% better classification accuracy than the present-day method..
Downloads
References
M. M. Islam, F. Karray, R. Alhajj and J. Zeng, "A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)," in IEEE Access, 2021, vol. 9, pp. 30551-30572.
A. Castiglione, P. Vijayakumar, M. Nappi, S. Sadiq and M. Umer, "COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network," in IEEE Transactions on Industrial Informatics, 2021, vol. 17, no. 9, pp. 6480-6488.
https://www.statista.com/statistics/1093256/novel-coronavirus-2019ncov-deaths-worldwide-by-country.
S Rahman, S Sarker, MAA Miraj, RA Nihal,” Deep learning–driven automated detection of Covid-19 from radiography images: A comparative analysis”, Cognitive Computation, Springer 2021.
J Somasekar, PP Kumar, A Sharma, “Machine learning and image analysis applications in the fight against COVID-19 pandemic: Datasets, research directions, challenges and opportunities”, Proceedings in Matrials Today, Elsevier 2021.
J. De Moura et al., "Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices," in IEEE Access, 2020, vol. 8, pp. 195594-195607
S Kumar, RD Raut, BE Narkhede,” A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers”, International Journal of Healthcare Management, Taylor and Francis, 2020, vol.13, no.4, pp. 337-345.
JB Awotunde, SO Folorunso, RG Jimoh, “Application of artificial intelligence for COVID-19 epidemic: an exploratory study, opportunities, challenges, and future prospects”, Artificial Intelligence for COVID-19. Studies in Systems, Decision and Control, Springer, 2021, vol 358.
S. Hu et al., "Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification From CT Images," in IEEE Access, 2020, vol. 8, pp. 118869-118883.
J. C. Clement, V. Ponnusamy, K. C. Sriharipriya and R. Nandakumar, "A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis," in IEEE Reviews in Biomedical Engineering, 2022, vol. 15, pp. 325-340.
H. S. Alghamdi, G. Amoudi, S. Elhag, K. Saeedi and J. Nasser, "Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey," in IEEE Access, 2021, vol. 9, pp. 20235-20254.
T Ozturk, M Talo, EA Yildirim, UB Baloglu, “Automated detection of COVID-19 cases using deep neural networks with X-ray images”, Computers and Biology and Medicine, Elsevier 2020, col.121, 103792.
D. Dong et al., "The Role of Imaging in the Detection and Management of COVID-19: A Review," in IEEE Reviews in Biomedical Engineering, 2021, vol. 14, pp. 16-29.
K. Foysal Haque, F. Farhan Haque, L. Gandy and A. Abdelgawad, "Automatic Detection of COVID-19 from Chest X-ray Images with Convolutional Neural Networks," 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE), 2020, pp. 125-130.
A Rehman, MA Iqbal, H Xing, I Ahmed, “COVID-19 detection empowered with machine learning and deep learning techniques: A systematic review”, Applied Sciences, MDPI 2021, vol.11, no.8, 3414.
S Goyal, R Singh, “Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques”, Journal of Ambient Intelligence and Humanized Computing, Springer 2023, vol,14, pp. 3239–3259
L Hussain, T Nguyen, H Li, AA Abbasi, KJ Lone, “Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection”, BioMedical Engineering OnLine, Springer, 2020, vol.19, no.88.
M Roberts, D Driggs, M Thorpe, J Gilbey, “Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans”, Nature Machine Intelligence, 2021, vol.3, pp.199–217.
S Jamil, N Mark, G Carlos, CSD Cruz, “Diagnosis and management of COVID-19 disease”, American Journal of Respiratory and Critical Care Medicine, 2020, vol.201, pp.19-22.
J. C. Clement, V. Ponnusamy, K. C. Sriharipriya and R. Nandakumar, "A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis," in IEEE Reviews in Biomedical Engineering, 2022, vol. 15, pp. 325-340
M. M. Islam, F. Karray, R. Alhajj and J. Zeng, "A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)," in IEEE Access, vol. 9, pp. 30551-30572
A Akbarimajd, N Hoertel, MA Hussain, “Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images”, Journal of Computational Science, Elsevier 2022, vol.63, 101763.
MR Hassan, WN Ismail, A Chowdhury, “A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19”, The Journal of Supercomputing, Springer 2022, vol.78, pp.10250–10274.
ML Huang, YC Liao, “A lightweight CNN-based network on COVID-19 detection using X-ray and CT images”, Computers in biology and medicne, Elsevier 2022, vol.146, 105604.
MF Aslan, K Sabanci, A Durdu, MF Unlersen, “COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization”, Computers in biology and medicine, Elsevier 2022, vol.142, 105244.
M Momeny, AA Neshat, MA Hussain, S Kia, “Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images”, Computers in Biology and Medicine, Elsevier, 2022, vol.136, 104704.
MF Aslan, MF Unlersen, K Sabanci, A Durdu, “CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection”, Applied Soft Computing, Elsevier 2021, vol. 98, 106912.
R Kundu, PK Singh, S Mirjalili, R Sarkar, “COVID-19 detection from lung CT-Scans using a fuzzy integral-based CNN ensemble”, Computers in biology and medicine, Elsevier 2022, vol.138, 104895.
S Pathan, PC Siddalingaswamy, T Ali, “Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture”, Biomedical Signal Processing and Control, Elsevier, 2022, vol.64, 102365.
NN Das, N Kumar, M Kaur, V Kumar, D Singh, “Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays”, IBRM, Elsevier, 2022, vol.43, no.2, pp.114-119.
D. A. Zebari, A. M. Abdulazeez, D. Q. Zeebaree and M. S. Salih, "A Fusion Scheme of Texture Features for COVID-19 Detection of CT Scan Images," 2020 International Conference on Advanced Science and Engineering (ICOASE), 2020, pp. 1-6.
O. El Gannour, S. Hamida, B. Cherradi, A. Raihani And H. Moujahid, "Performance Evaluation of Transfer Learning Technique for Automatic Detection of Patients with COVID-19 on X-Ray Images," 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS), 2020, pp. 1-6.
M Toğaçar, B Ergen, Z Cömert, “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 and Biology in Medicine, Elsevier 2020, vol.21, no. 103805.
SH Kassania, PH Kassanib, MJ Wesolowskic, “Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach”, Biometrics and Biomedical Engineering, Elsevier, 2021, vol.41, no.3, pp. 867-879.
V Bolon-Canedo, B Remeseiro, “Feature selection in image analysis: a survey”, Artificial Intelligence Review, Springer 2020, vol. 53, pp. 2905–2931.
R. Miikkulainen, J Liang, E Meyerson, A Rawal, “Evolving deep neural networks”, Artifical Intelligence in the Age of Neural Networks and Brain Computing, Academic Press, 2019, pp.293-312.
A Zargari Khuzani, M Heidari, SA Shariati, “COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images”, Scientific Repots, Nature, 2021, vol.11, no. 9887.
M Shafiq‐ul‐Hassan, GG Zhang, K Latifi, “Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels”, Scientific Reports, Nature, 2018, vol.8, no. 10545.
M Toğaçar, B Ergen, Z Cömert, “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, Elsevier 2020, vol.121, 103805.
SH Kassania, PH Kassanib, MJ Wesolowskic, “Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach”, Biometrics and Biomedical Engineering, Elsevier 2021, vol.41, no.3, pp. 867-879.
E. -S. M. El-Kenawy et al., "Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification," in IEEE Access, 2021, vol. 9, pp. 36019-36037.
K. K. Singh and A. Singh, "Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network," in Big Data Mining and Analytics, 2021, vol. 4, no. 2, pp. 84-93.
B. King, S. Barve, A. Ford and R. Jha, "Unsupervised Clustering of COVID-19 Chest X-Ray Images with a Self-Organizing Feature Map," 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS), 2020, pp. 395-398.
ALA Dalal, MAA Al-qaness, Z Cai, EA Alawamy, “IDA: Improving distribution analysis for reducing data complexity and dimensionality in hyperspectral images”, Pattern Recogniton, Elsevier 2023, vol.134, no. 109096.
R Mostafiz, MS Uddin, MM Reza, MM Rahman, “Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features”, Journal of King Saud University - Computer and Information Sciences, Elsevier 2022, vol.34, no.6, Part B, pp. 3226-3235.
U Muhammad, MZ Hoque, M Oussalah, “SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images”, Lnowledge Based Systems, Elsevier 2022, vol.241, 108207.
I. Ahmad, M. Basheri, M. J. Iqbal and A. Rahim, "Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection," in IEEE Access 2018, vol. 6, pp. 33789-33795.
P. Padilla, M. Lopez, J. M. Gorriz, J. Ramirez, D. Salas-Gonzalez and I. Alvarez, "NMF-SVM Based CAD Tool Applied to Functional Brain Images for the Diagnosis of Alzheimer's Disease," in IEEE Transactions on Medical Imaging, 2012 vol. 31, no. 2, pp. 207-216
G. Rathee, S. Garg, G. Kaddoum, Y. Wu, D. N. K. Jayakody and A. Alamri, "ANN Assisted-IoT Enabled COVID-19 Patient Monitoring," in IEEE Access, vol. 9, pp. 42483-42492.
C. Zhou, J. Song, S. Zhou, Z. Zhang and J. Xing, "COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images," in IEEE Access, 2021, vol. 9, pp. 81902-8191.
S. K. Zhou et al., "A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises," in Proceedings of the IEEE, 2021, vol. 109, no. 5, pp. 820-838.
S. Tiwari, P. Chanak and S. K. Singh, "A Review of the Machine Learning Algorithms for Covid-19 Case Analysis," in IEEE Transactions on Artificial Intelligence.
T. Sercu, C. Puhrsch, B. Kingsbury and Y. LeCun, "Very deep multilingual convolutional neural networks for LVCSR," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016, pp. 4955-4959.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
