A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction

Main Article Content

Bhautik Daxini
Dr. M.K. Shah
Rutvik K. Shukla
Dr. Rohit Thanki
Viral Thakar,

Abstract

Deep learning methodologies are now feasible in practically every sphere of modern life because to technological advancements. Because of its high level of accuracy, deep learning can automatically diagnose and classify a wide variety of medical conditions in the field of medicine. The coronavirus first appeared in Wuhan, China, in December 2019, and quickly spread throughout the world. The pandemic of COVID-19 presented significant challenges to the world’s health care system. PCR and medical imaging can diagnose COVID-19. There has a negative impact on the health of people as well as the global economy, education, and social life. The most significant challenge in stymieing the rapid propagation of the disease is locating positive Corona patients as promptly as possible. Because there are no automated tool kits, additional diagnostic equipment will be required. According to radiological studies, these images include important information about the coronavirus. Accurate treatment of this virus and a solution to the problem of a lack of medical professionals in remote areas may be possible with the help of a specialized Artificial Intelligence (AI) system and radiographic pictures. We used pre-trained CNN models Xception, Inception, ResNet-50, ResNet-50V2, DenseNet121, and MobileNetV2 to correct the COVID-19 classification analytics. In this paper, we investigate COVID-19 detection methods that make use of chest X-rays. According to the findings of our research, the pre-trained CNN Model that makes use of MobileNetV2 performs better than other CNN techniques in terms of both the size of the solution and its speed. Our method might be of use to researchers in the process of fine-tuning the CNN model for efficient COVID screening.

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[1]
Bhautik Daxini, Dr. M.K. Shah, Rutvik K. Shukla, Dr. Rohit Thanki, and Viral Thakar, , Trans., “A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction”, IJITEE, vol. 12, no. 9, pp. 1–16, Aug. 2023, doi: 10.35940/ijitee.I9696.0812923.
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Author Biographies

Bhautik Daxini, Research Scholar, Department of Instrumentation and Control, Gujarat Technological University, Ahmedabad (Gujarat), India.

Bhautik Daxini is a research scholar in Instrumentation & Control Engineering Department of Gujarat Technological University, Ahmedabad, Gujarat. He has completed his M.Tech in Control System (2013) from Manipal Institute of Technology, Manipal, Karnataka. He has done his B.E. in Biomedical & Instrumentation (2008) from C.U. Shah College of Engineering & Technology, Surendranagar, Gujarat. He has received Gold Medal in his Master’s as well as in Bachelor’s Degree. He is at presently working as an Assistant Prof. in the Department of Instrumentation & Control at Shantilal Shah Govt. Engineering College, Bhavnagar. He has overall experience of about 10+ years in the academic field. His research interest include artificial intelligence, deep learning, medial image analysis, Control System and Biomedical Instrumentation.

Dr. M.K. Shah, Associate Prof. & Head, Department of Instrumentation & Control Engineering, Vishwakarma Government Engineering College, Chandkheda, (Gujarat), India.

Dr. M.K. Shah is an Associate Prof. and Head at Instrumentation & Control Engineering Dept. at VGEC – Chandkheda, Gujarat. He has completed his PhD (2016) from Gujarat Technological University and M.E.in Instrumentation and Control (2001) from Thapar University. He has completed his B.E. in Instrumentation & Control Engineering (1993) from L.D. Engineering College. He is having more than 29 years of teaching experience. He has received university gold medal for securing first rank in PG program at Thapar University. His research area includes artificial intelligence, deep learning, NIR spectroscopy, control and image processing. He has undertaken various responsibilities at various levels in his academic life.

Rutvik K. Shukla, Assistant Prof., Department of Instrumentation & Control Engineering, Government Engineering College, Rajkot (Gujarat), India.

Rutvik K. Shukla received his ME in Automatic control & Robotics from the Maharaja Sayajirav University (MSU), Baroda, Gujarat in 2009. In 2011 he was selected through public service commission in Government of Gujarat for the post of Assistant Professor in Vishwakarma Government Engineering college, chankheda, Ahmedabad, Gujarat. Later in the year 2019, he completed his PhD from Gujarat Technological University under the guidance of Dr. C. B. Bhatt. Some of his research area includes Biomedical engineering, Expert system, Ontology Engineering. He also has interest in the areas of deep learning, image processing and medical imaging. He 17 years of experience in the academic field.

Dr. Rohit Thanki, Data Scientist, KRiAN GmbH, Wolfsburg, Germany.

Dr. Rohit Thanki is an experienced computer vision expert and AI researcher with over 8 years of expertise in medical image analysis, biometrics, data security, and artificial intelligence. He has more than 3 years of academic experience in various engineering institutions in India and is currently working as a Data Scientist at KRiAN GmbH, Germany. Previously, he held the position of Head of Research & Development at Prognica Labs Tech FZCO, Dubai, UAE, and served as a consultant for Enno venture Technologies Private Limited, Bengaluru, India. Dr. Thanki holds a bachelor's degree in Electronics & Communication, a master’s degree in Communication Engineering, and a doctorate in Electronics & Communication with a specialization in digital image processing and data security. His research interests include medical image analysis, artificial intelligence, machine learning, deep learning, digital watermarking, data security, compressive sensing, and signal processing. He has authored over 40 publications in reputed journals with high impact factors and international conferences indexed in Web of Science and Scopus. Furthermore, Dr. Thanki has contributed to over 20 books with respected publishers such as Springer, CRC Press, Elsevier, De Gruyter, and IGI Global. He has also served as a reviewer for various reputable journals, including IEEE Transactions on Audio, Speech and Natural Language Processing, ACM Transactions on Multimedia Computing, Communications and Applications, IEEE Consumer Electronics Magazine, IEEE Access, IEEE Journal of Biomedical and Health Informatics, Signal Processing: Image Communication, Pattern Recognition, Computers and Electrical Engineering, Informatics in Medicine, Journal of Ambient Intelligence and Humanized Computing, IET Biometrics, and IET Image Processing, among others.

Viral Thakar,, Senior Machine Learning Engineer, Autodesk, Toronto, Ontario, Canada.

Viral Thakar is a highly skilled Machine Learning Research Engineer with expertise in computer vision, natural language processing, and IoT. He has a strong background in both research and applied engineering, with a focus on unsupervised domain adaptation, self-supervised learning, structured and unstructured as well as euclidean and non-euclidean data and one-shot learning. Viral has successfully contributed to the development of multidisciplinary AI-based solutions and has been recognized for his work in the field. With expertise in machine learning on edge devices and image and video signal processing, His interest area also includes, generative models, graph neural networks and convolutional neural networks. He did his M.Tech in Communication System Engineering (2013) from Charotar University of Science and Technology. He completed his B.E. Electronics and Communication (2011) from Atmiya Institute of Technology & Science.

How to Cite

[1]
Bhautik Daxini, Dr. M.K. Shah, Rutvik K. Shukla, Dr. Rohit Thanki, and Viral Thakar, , Trans., “A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction”, IJITEE, vol. 12, no. 9, pp. 1–16, Aug. 2023, doi: 10.35940/ijitee.I9696.0812923.
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