Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective
DOI:
https://doi.org/10.24203/ajas.v10i3.6998Keywords:
COVID-19, Deep Learning, Machine Learning, Artificial Intelligence, Chest X-ray, CT-scanAbstract
Today, humans live in the era of rapid growth in electronic devices that are based on artificial intelligence, including the significant growth in the manufacture of machines that perform intelligent human tasks to solve complex situations. Artificial intelligence will significantly influence the development of many domains, especially the medical domain, which relies heavily on artificial intelligence techniques in diagnosing disease data and manufacturing drugs and vaccines. Artificial intelligence has unexpectedly advanced in helping physicians and healthcare workers save many lives, especially during the spread of the COVID-19 virus. This article reviews some literature that have applied deep learning techniques to detect COVID-19 based on chest x-rays and CT-scans images. This article concluded that deep learning techniques have a fundamental and significant role in diagnosing a big dataset of images and assisting specialists in determining whether a person is infected (positive cases).
References
Sarkar M. and Pal S. C., “Human health hazard assessment for high groundwater arsenic and fluoride intact in Malda district, Eastern India,” Groundwater for Sustainable Development, vol.13, pp:100565, May 2021. https://doi.org/10.1016/j.gsd.2021.100565
Feng R., Wang F., Wang K., Wang H., and Li L., “Urban ecological land and natural-anthropogenic environment interactively drive surface urban heat island: An urban agglomeration-level study in China,” Environment International, vol.157, pp:106857, December 2021. https://doi.org/10.1016/j.envint.2021.106857
Pradhan B., Bhattacharyya S., and Pal K., “IoT-Based Applications in Healthcare Devices,” Journal of Healthcare Engineering, vol. 2021, Article ID 6632599, pp:1-18, March 2021. https://doi.org/10.1155/2021/6632599
Oladapo B. I., Ismail S. O., Afolalu T. D., Olawade D. B., and Zahedi M., “Review on 3D printing: Fight against COVID-19,” Materials Chemistry and Physics, vol.285, pp:123943, January 2021. https://doi.org/10.1016/j.matchemphys.2020.123943
Mijwil M. M., Abttan R. A., and Alkhazraji A., “Artificial intelligence for COVID-19: A Short Article, Asian Journal of Pharmacy, Nursing and Medical Sciences, vol.10, no.1, pp:1-6, May 2022. https://doi.org/10.24203/ajpnms.v10i1.6961
Mijwil M. M., Al-Mistarehi AH., Zahran D. J., Alomari S., and Doshi R., “Spanish Flu (Great Influenza) 1918: The Tale of The Most deadly Pandemic in History,” Asian Journal of Applied Sciences, vol.10, no.2, pp:109-115, May 2022. https://doi.org/10.24203/ajas.v10i2.6949
Saadat S., Tehrani Z. R., Logue J., Newman M., Frieman M. B., Harris A. D., et al., “Binding and Neutralization Antibody Titers After a Single Vaccine Dose in Health Care Workers Previously Infected With SARS-CoV-2,” JAMA, vol.325,no.14, pp:1467-1469, March 2021. https://doi.org/10.1001/jama.2021.3341
Mijwil M. M., Al-Mistarehi AH., and Aggarwal K., “The Effectiveness of Utilising Modern Artificial Intelligence Techniques and Initiatives to Combat COVID-19 in South Korea: A Narrative Review,” Asian Journal of Applied Sciences, vol.9, no.5, pp:343-352, November 2021. https://doi.org/10.24203/ajas.v9i5.6753
Chowdhury P., Paul S. K., Kaisar S., Moktadir A., “COVID-19 pandemic related supply chain studies: A systematic review,” Transportation Research Part E: Logistics and Transportation Review, vol.148, pp:102271, April 2021. https://doi.org/10.1016/j.tre.2021.102271
He W., Zhang Z., and Li W., “Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic,” International Journal of Information Management, vo.57, pp:102287, April 2021. https://doi.org/10.1016/j.ijinfomgt.2020.102287
Mijwil M. M., Shukur B. S., and Mahmood E. Sh.,“The Most Common Heart Diseases and Their Influence on Human Life: A Mini-review,” Journal of Advances in Medicine and Medical Research, vol.34, no.15, pp:26-36,May 2022. https://doi.org/10.9734/jammr/2022/v34i1531396
Machida M., Nakamura I., Kojima T., Saito R., Nakaya T., Hanibuchi T., “Acceptance of a COVID-19 Vaccine in Japan during the COVID-19 Pandemic,” Vaccines, vol.9, no.3, pp:1-11, March 2021. https://doi.org/10.3390/vaccines9030210
Mitze T., Kosfeld R., Rode J., and Wälde K., “Face masks considerably reduce COVID-19 cases in Germany,” Proceedings of the National Academy of Sciences, vol.117, no.51, pp:32293-32301. https://doi.org/10.1073/pnas.2015954117
Ramaiah G. B., Tegegne A., and Melese B., “Functionality of nanomaterials and its technological aspects – Used in preventing, diagnosing and treating COVID-19,” Materials Today: Proceedings, vol.47, pp:Pages 2337-2344, January 2021. https://doi.org/10.1016/j.matpr.2021.04.306
Privitera M. B., Evans M., Southee D., “Human factors in the design of medical devices – Approaches to meeting international standards in the European Union and USA,” Applied Ergonomics, vol.59, pp:251-263, March 2017. https://doi.org/10.1016/j.apergo.2016.08.034
Ahmad R. W., Salah K., Jayaraman R., Yaqoob I., Omar M., Ellahham S., “Blockchain-Based Forward Supply Chain and Waste Management for COVID-19 Medical Equipment and Supplies,” IEEE Access, vol.9, pp:44905 - 44927, March 2021. https://doi.org/10.1109/ACCESS.2021.3066503
Karthick R., Ramkumar R., Akram M., Kumar M. V., “Overcome the challenges in bio-medical instruments using IOT – A review,” Materials Today: Proceedings, vol.45, pp:1614-1619, 2021. https://doi.org/10.1016/j.matpr.2020.08.420
Aggarwal K., Mijwil M. M., Sonia, Al-Mistarehi AH., Alomari S., Gök M., Alaabdin A. M., and Abdulrhman, S. H., “Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning,” Iraqi Journal for Computer Science and Mathematics, vol.3, no.1, pp:115-123, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.013
Yedavalli V. S., Tong E., Martin D., Yeom K. W., and Forkert N. D., “Artificial intelligence in stroke imaging: Current and future perspectives,” Clinical Imaging, vol.69, pp:246-254, January 2021. https://doi.org/10.1016/j.clinimag.2020.09.005
Faieq A. K., and Mijwil M. M., “Prediction of heart diseases utilising support vector machine and artificial neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol.26, no.1, pp:374-380, April 2022. http://doi.org/10.11591/ijeecs.v26.i1.pp374-380
Secinaro S., Calandra D., Secinaro A., Muthurangu V., and Biancone P., “The role of artificial intelligence in healthcare: a structured literature review,” BMC Medical Informatics and Decision Making, vol. 21, no. 125, pp:1-23, April 2021. https://doi.org/10.1186/s12911-021-01488-9
Abd S. N., Alsajri M., and Ibraheem H. R., “Rao-SVM Machine Learning Algorithm for Intrusion Detection System,” Iraqi Journal For Computer Science and Mathematics, vol.1, no.1, pp:23-27, January 2020. https://doi.org/10.52866/ijcsm.2019.01.01.004
Mijwil M. M., Salem I. E, and Abttan R. A. “Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets,” Asian Journal of Computer and Information Systems, vol.9, no.5, pp:29-34, November 2021, https://doi.org/10.24203/ajcis.v9i4.6765
Hasan A., Al-Jilawi A. S., and Alsharify F. H. A., “Review of Mathematical Modelling Techniques with Applications in Biosciences,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.1, pp:135-144, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.015
Kapoor R., Walters S. P., and Al-Aswad L. A., “The current state of artificial intelligence in ophthalmology,” Survey of Ophthalmology, vol.64, pp:233-240, April 2019. https://doi.org/10.1016/j.survophthal.2018.09.002
Rasheed J., Jamil A., Hameed A. A., Aftab U., Aftab J., Shah S. A., and Draheim D., “A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic,” Chaos, Solitons & Fractals, vol.141, pp:110337, December 2020. https://doi.org/10.1016/j.chaos.2020.110337
Kaur I., Behl T., Aleya L., Rahman H., Kumar A., Arora S., and Bulbul I. J., “Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic,” Environmental Science and Pollution Research, vol. 28, pp: 40515–40532, May 2021. https://doi.org/10.1007/s11356-021-13823-8
Javaid M., Haleem A., Vaishya R., Bahl S., Suman R., Vaish A., “Industry 4.0 technologies and their applications in fighting COVID-19 pandemic,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol.14, no.4, pp:419-422, August 2020. https://doi.org/10.1016/j.dsx.2020.04.032
Sabah N., Sagheer A., and Dawood O., “Survey: (Blockchain-Based Solution for COVID-19 and Smart Contract Healthcare Certification),” Iraqi Journal For Computer Science and Mathematics, vol.2, no.1, pp:1-8, January 2021. https://doi.org/10.52866/ijcsm.2021.02.01.001
Haleem A., Javaid M., Singh R. P., and Suman R., “Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic,” Sustainable Operations and Computers, vol.2, pp:71-78, 2021. https://doi.org/10.1016/j.susoc.2021.04.003
Jabarulla M. Y. and Lee H., “A Blockchain and Artificial Intelligence-Based, Patient-Centric Healthcare System for Combating the COVID-19 Pandemic: Opportunities and Applications,” Healthcare, vol.9, no.8, pp:1-22, August 2021. https://doi.org/10.3390/healthcare9081019
Tan L., Yu K., Bashir A. K., Cheng X., Ming F., Zhao L., and Zhou X., “Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach,” Neural Computing and Applications, vol.2021, pp:1-14, July 2021. https://doi.org/10.1007/s00521-021-06219-9
Fourcade A. and R.H. Khonsari R. H., “Deep learning in medical image analysis: A third eye for doctors, Journal of Stomatology,” Oral and Maxillofacial Surgery, vol.120, no.4, pp:279-288, September 2019. https://doi.org/10.1016/j.jormas.2019.06.002
Qi Y. and Liu C., “Deep Learning for Medical Materials: Review and Perspective,” ES Materials & Manufacturing, vol.12, pp:17-28, February 2021. https://doi.org/10.30919/esmm5f426
Al-mashhadani M. I., Hussein K. M., and Khudir E. T., “Sentiment Analysis using Optimized Feature Sets in Different Facebook/Twitter Dataset Domains using Big Data,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.1, pp:64-70, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.007
Al-Shahwani H. I. W., Yassin W. M., Zainalabidin Z., and Rasheed M., “An integrated multi layers approach for detecting unknown malware behaviours,” International Journal of Engineering & Technology, vol.7, no.4, pp:5618-5621, 2018. https://doi.org/10.14419/ijet.v7i4.23675
Hiran K. K. and Doshi R., “An Artificial Neural Network Approach for Brain Tumor Detection Using Digital Image Segmentation,” International Journal of Emerging Trends & Technology in Computer Science, vol.2, no.5, pp:227-231, October 2013.
Ramasamy J. and Doshi R., “Machine Learning in Cyber Physical Systems for Healthcare: Brain Tumor Classification From MRI Using Transfer Learning Framework,” Real-Time Applications of Machine Learning in Cyber-Physical Systems- IGI Global, pp:65-76, 2022. https://doi.org/10.4018/978-1-7998-9308-0.ch005
Latif S., Usman M., Manzoor S., Iqbal W., Qadir J., Tyson G., et al., “Leveraging Data Science to Combat COVID-19: A Comprehensive Review,” IEEE Transactions on Artificial Intelligence, vol.1, no.1, pp:85 - 103, September 2020. https://doi.org/10.1109/TAI.2020.3020521
Shaker A. S., Abdulqader A. W., Mijwil M. M., “DE-striping Hyperspectral Remote Sensing Images Using Deep Convolutional Neural Network”, Asian Journal of Applied Sciences, vol.9, no.4, pp:285-290. September 2021. https://doi.org/10.24203/ajas.v9i4.6719
Mijwil M. M. and Abttan R. A., “Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm,” Asian Journal of Applied Sciences, vol.9, no.1, pp:45-52, February 2021, https://doi.org/10.24203/ajas.v9i1.6503
Singh P. and Kaur R., “An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19,” Global Transitions, vol.2, pp:283-292, 2020. https://doi.org/10.1016/j.glt.2020.11.002
Kumar H., Soh P. J., and Ismail M. A., “Big Data Streaming Platforms: A Review,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.2, pp: 95-100, April 2022. https://doi.org/10.52866/ijcsm.2022.02.01.010
Al-Zubaidi E. A., Mijwil M. M., and Alsaadi A. S., “Two-Dimensional Optical Character Recognition of Mouse Drawn in Turkish Capital Letters Using Multi-Layer Perceptron Classification,” Journal of Southwest Jiaotong University, vol.54, no.4, pp.1-6, Augusts 2019. https://doi.org/10.35741/issn.0258-2724.54.4.4.
Mijwil M. M., and Abttan R. A., “Artificial Intelligence: A Survey on Evolution and Future Trends,” Asian Journal of Applied Sciences, vol.9, no.2, pp:87-93, April 2021. https://doi.org/10.24203/ajas.v9i2.6589
Rammo F. M. and Al-Hamdani M. N., “Detecting The Speaker Language Using CNN Deep Learning Algorithm,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.1, pp:43-52, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.005
Salem I. E., Mijwil M. M., Abdulqader A. W., and Ismaeel M. M., “Flight-Schedule using Dijkstra's Algorithm with Comparison of Routes Finding,” International Journal of Electrical and Computer Engineering, vol.12, no.2, pp:1675-1682, April 2022. http://doi.org/10.11591/ijece.v12i2.pp1675-1682
Tătaru O. S., Vartolomei M. D., Rassweiler J. J., Virgil O., Lucarelli G., Porpiglia F., et al., “Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives,” Diagnostics, vol.11, no.2, pp:1-20, February 2021. https://doi.org/10.3390/diagnostics11020354
Mijwil M. M., Mutar D. S., Filali Y., Aggarwal K., and Al-Shahwani H., “Comparison Between Expert Systems, Machine Learning, and Big Data: An Overview,” Asian Journal of Applied Sciences, vol.10, no.1, pp:83-88, March 2022. https://doi.org/10.24203/ajas.v10i1.6930
Thrall J. H., Li X., Li Q., Cruz C., Do S., DO K. D., and Brink J., “Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success,” Journal of the American College of Radiology, vol.15, no.3, pp:504-508, March 2018. https://doi.org/10.1016/j.jacr.2017.12.026
Gams M. and Kolenik T., “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules,” Electronics, vol.10, no.4, pp:1-16, February 2021. https://doi.org/10.3390/electronics10040514
Miller D. D. and Brown E. W., “Artificial Intelligence in Medical Practice: The Question to the Answer?,” The American Journal of Medicine, vol.131, no.2, pp:129-133, February 2018. https://doi.org/10.1016/j.amjmed.2017.10.035
Mijwil, M. M., “High Speed Transmission of Signal Level for White Light Emitting Diode (LED) as a Transmitter Device by using Modified Phase Equalization,” Indonesian Journal of Electrical Engineering and Computer Science, vol.17, no.3, pp.1348-1354, March 2020. https://doi.org/10.11591/ijeecs.v17.i3.
Diaz O., Kushibar K., Osuala R., Linardos A., Garrucho L., Igual L., et al., “Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools,” Physica Medica, vol.83, pp:25-37, March 2021. https://doi.org/10.1016/j.ejmp.2021.02.007
Zanca F.,Hernandez-Giron I., Avanzo M., Guidi G., Crijns W., Diaz O., et al., “Expanding the medical physicist curricular and professional programme to include Artificial Intelligence,” Physica Medica, vol.83, pp:174-183, March 2021. https://doi.org/10.1016/j.ejmp.2021.01.069
Mijwil M. M. and Salem I. E., “Credit Card Fraud Detection in Payment Using Machine Learning Classifiers,” Asian Journal of Computer and Information Systems, vol.8, no.4, pp:50-53, December 2020. https://doi.org/10.24203/ajcis.v8i4.6449
T R. S. and Sathya R., “Ensemble Machine Learning Techniques for Attack Prediction in NIDS Environment,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.2, pp:78-82, March 2022. https://doi.org/10.52866/ijcsm.2022.02.01.008
BPharm G. C. and Rohren E., “Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning,” Seminars in Nuclear Medicine, vol.51, no.2, pp:102-111, March 2021. https://doi.org/10.1053/j.semnuclmed.2020.08.002
Mijwil M. M., “Malware Detection in Android OS Using Machine Learning Techniques,” Data Science and Applications, vol.3, no.2, pp:5-9, 31 December 2020.
Jena B., Saxena S., Nayak G. K., Saba L., Sharma N., Suri J. S., “Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review,” Computers in Biology and Medicine, vol.137, pp:104803, October 2021. https://doi.org/10.1016/j.compbiomed.2021.104803
Niu Y. and Korneev A., “Identification Method of Power Internet Attack Information Based on Machine Learning,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.2, pp:1-7, February 2022. https://doi.org/10.52866/ijcsm.2022.02.01.001
Mijwil M. M., Aggarwal K., Mutar D. S., Mansour N., and Singh R. S. S., “The Position of Artificial Intelligence in the Future of Education: An Overview,” Asian Journal of Applied Sciences, vol.10, no.2, pp:102-108, May 2022. https://doi.org/10.24203/ajas.v10i2.6956
Bansal A., Padappayil R. P., Garg C., Singal A., Gupta M., and Klein A., “Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review,” Journal of Medical Systems, vol. 44, no. 156, pp: 1-6, August 2020. https://doi.org/10.1007/s10916-020-01617-3
Mijwil M. M., Al-Mistarehi AH, and Mutur D. S., “The Practices of Artificial Intelligence Techniques and Their Worth in the Confrontation of COVID-19 Pandemic: A Literature Review, " Mobile and Forensics, vol.4, no.1, pp:11-30, March 2022. http://dx.doi.org/10.12928/mf.v4i1.5691
Zhang L., Tan J., Han D., and Zhu H., “From machine learning to deep learning: progress in machine intelligence for rational drug discovery,” Drug Discovery Today, vol.22, no.11, pp:1680-1685, November 2017. https://doi.org/10.1016/j.drudis.2017.08.010
Farhan B. I. and Jasim A. D., “A Survey of Intrusion Detection Using Deep Learning in Internet of Things,” Iraqi Journal For Computer Science and Mathematics, vol.3, no.1, pp:83-93, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.009
Middleton M., Deep Learning vs. Machine Learning — What’s the Difference?, Flatiron School, February 2021, link: https://flatironschool.com/blog/deep-learning-vs-machine-learning/,
Fauw J., Ledsam J. R., Romera-Paredes B., Nikolov S., Tomasev N., Blackwell S., et al., “Clinically applicable deep learning for diagnosis and referral in retinal disease,” Nature Medicine, vol. 24, pp:1342-1350, August 2018. https://doi.org/10.1038/s41591-018-0107-6
Aggarwal K., Bhamrah M. S., and Ryait H. S., “The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization,” SpringerPlus, vol.5, no. 322, pp:1-15, March 2016. https://doi.org/10.1186/s40064-016-1970-6
Araújo T., Aresta G., Castro E., Rouco J., Aguiar P., Eloy C., et al., “Classification of breast cancer histology images using Convolutional Neural Networks,” Plos One, vol.12, no.6, pp:e0177544, June 2017. https://doi.org/10.1371/journal.pone.0177544
Mahrishi, M., Hiran, K. K., Meena, G., and Sharma, P. (Eds.). (2020). Machine Learning and Deep Learning in Real-Time Applications. IGI global. https://doi.org/10.4018/978-1-7998-3095-5
Lakhwani, K., Bhargava, S., Hiran, K. K., Bundele, M. M., and Somwanshi, D. (2020, December). Prediction of the onset of diabetes using artificial neural network and pima indians diabetes dataset. In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). IEEE. https://doi.org/10.1109/ICRAIE51050.2020.9358308
Hiran, K. K., Jain, R. K., Lakhwani, K., and Doshi, R. (2021). Machine Learning: Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition). BPB Publications.
Lakhwani, K., Bhargava, S., Somwanshi, D., Doshi, R., & Hiran, K. K. (2020, December). An Enhanced Approach to Infer Potential Host of Coronavirus by Analyzing Its Spike Genes Using Multilayer Artificial Neural Network. In 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-5). IEEE. https://doi.org/10.1109/ICRAIE51050.2020.9358382
Mijwil M M. and Aggarwal K., “A diagnostic testing for people with appendicitis using machine learning techniques,” Multimedia Tools and Applications, viol. 81, pp:7011-7023, January 2022. https://doi.org/10.1007/s11042-022-11939-8
Aggarwal K., Bhamrah M. S., and Ryait H. S., “Detection of cirrhosis through ultrasound imaging by intensity difference technique,” EURASIP Journal on Image and Video Processing, vol. 2019, no.80, pp:1-10, September 2019. https://doi.org/10.1186/s13640-019-0482-z
Zhang W., Li C., Peng G., Chen Y., and Zhang Z., “A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load,” Mechanical Systems and Signal Processing, vol.100, pp:439-453, February 2018. https://doi.org/10.1016/j.ymssp.2017.06.022
Thakkar A. and Chaudhari K., “A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions,” Expert Systems with Applications, vol.177, pp:114800, September 2021. https://doi.org/10.1016/j.eswa.2021.114800
Mijwil M. M., “Iraqi Food Image Detection Using Convolutional Neural Network Classification Method,” Lecture Notes in Networks and Systems, In Book Title: Proceedings of the International Conference on Computing and Communication Systems, vol.170, pp:249-257, April 2021. https://doi.org/10.1007/978-981-33-4084-823,
Aggarwal K. and Ryait H. S., “Ultrasound Image Analysis of Cirrhosis Liver Disease Using SVM Classifier,” International Journal of Advanced Research in Computer Science and Software Engineering, vol.3, no. 9, pp:63-69.
Alsharef A, Aggarwal K, Koundal D, Alyami H and Ameyed D, “An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding”, Computational Intelligence and Neuroscience- Hindwai, vol.2022, Feb, 2022.
Kousik N., Natarajan Y., R. Raja A., Kallam S., Patan R., and Gandomi A. H., “Improved salient object detection using hybrid Convolution Recurrent Neural Network,” Expert Systems with Applications, vol.166, pp:114064, March 2021. https://doi.org/10.1016/j.eswa.2020.114064
Vu M. T., Jardani A., Massei N., and Fournier M., “Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network,” Journal of Hydrology, vol.597, pp:125776, June 2021. https://doi.org/10.1016/j.jhydrol.2020.125776
Mijwil M. M., Alsaadi, A. S, and Aggarwal K., “Differences and Similarities Between Coronaviruses: A Comparative Review,” Asian Journal of Pharmacy, Nursing and Medical Sciences, vol.9, no.4, pp:49-61. September 2021. https://doi.org/10.24203/ajpnms.v9i4.6696
Durankuş F. and Aksu E., “Effects of the COVID-19 pandemic on anxiety and depressive symptoms in pregnant women: a preliminary study,” The Journal of Maternal-Fetal & Neonatal Medicine, vol.35, no.2, pp:205-211, May 2020. https://doi.org/10.1080/14767058.2020.1763946
Guefrechi S., Jabra M. B., Ammar A., Koubaa A., and Hamam H., “Deep learning based detection of COVID-19 from chest X-ray images,” Multimedia Tools and Applications, vol. 80, pp: 31803–31820, July 2021. https://doi.org/10.1007/s11042-021-11192-5
Mijwil M. M., “Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans,” Iraqi Journal of Science, vol.62, no.6., pp: 2099-2109, July 2021. https://doi.org/10.24996/ijs.2021.62.6.35
Nasiri H. and Hasani S., “Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost,” Radiography, In press, March 2022. https://doi.org/10.1016/j.radi.2022.03.011
Moura J., Novo J., and Ortega M., “Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images,” Applied Soft Computing, vol. 115, pp:108190, January 2022. https://doi.org/10.1016/j.asoc.2021.108190
Kamil M. Y., “A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images,” International Journal of Electrical and Computer Engineering, vol.11, no.1, pp:844-850, February 2021. https://doi.org/10.11591/ijece.v11i1.pp844-850
Wang L., Lin Z. Q., and Wong A.,” COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Scientific Reports, vol. 10, no.19549, pp:1-12, November 2020. https://doi.org/10.1038/s41598-020-76550-z
Oh Y., Park S., and Ye J. C., “Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets,” IEEE Transactions on Medical Imaging, vol.39, no.8, pp:2688 - 2700, May 2020. https://doi.org/10.1109/TMI.2020.2993291
He X., Yang X., Zhang S., Zhao J., Zhang Y., Xing E., and Xie P., “Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans,” Medrxiv, pp:1-10, April 2020. https://doi.org/10.1101/2020.04.13.20063941
Vaid S., Kalantar R., and Bhandari M., “Deep learning COVID-19 detection bias: accuracy through artificial intelligence,” International Orthopaedics, vol. 44, pp: 1539-1542, May 2020. https://doi.org/10.1007/s00264-020-04609-7
Jain R., Gupta M., Taneja S., and Hemanth D. J., “Deep learning based detection and analysis of COVID-19 on chest X-ray images,” Applied Intelligence, vol. 51, pp:1690-1700, October 2020. https://doi.org/10.1007/s10489-020-01902-1
Mijwil M. M. and Al-Zubaidi E. A., “Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks,” Iraqi Journal of Science, vol.62, no.8, pp: 2740-2747, August 2021. https://doi.org/10.24996/ijs.2021.62.8.27
Ismael A. M. and Şengür A., “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Expert Systems with Applications, vol.164, pp:114054, February 2021. https://doi.org/10.1016/j.eswa.2020.114054
Shah V., Keniya R., Shridharani A., Punjabi M., Shah J., and Mehendale N., “Diagnosis of COVID-19 using CT scan images and deep learning techniques,” Emergency Radiology, vol. 28, pp:497-505, February 2021. https://doi.org/10.1007/s10140-020-01886-y
Serte S. and Demirel H., “Deep learning for diagnosis of COVID-19 using 3D CT scans,” Computers in Biology and Medicine, vol.132, pp:104306, May 2021. https://doi.org/10.1016/j.compbiomed.2021.104306
Zhou T., Lu H., Yang Z., Qiu S., Huo B., and Dong Y., “The ensemble deep learning model for novel COVID-19 on CT images,” Applied Soft Computing, vol.98, pp:106885, January 2021. https://doi.org/10.1016/j.asoc.2020.106885
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Maad M. Mijwil, Karan Aggarwal, Ruchi Doshi, Kamal Kant Hiran, B. Sundaravadivazhagan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-NonCommercial 4.0 International that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.