Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Author:

Chandy D Abraham1,Babu Caren2,Manohar O Rahul2

Affiliation:

1. Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu 641114, India

2. Department of Electronics and Communication Engineering, Christ College of Engineering, Irinjalakuda, India

Abstract

Abstract: Deep learning is a prominent method for automatic detection of COVID-19 disease using a medical dataset. This paper aims to give a perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study of the available datasets comprising CT and X-ray images is presented in this paper, which can be very much useful in the context of a deep learning framework for COVID-19 detection. Moreover, various data handling techniques that are very essential in deep learning models are discussed in detail. Advanced data handling techniques and approaches to modify deep learning models are suggested to handle the data insufficiency problem in deep learning based on COVID-19 detection.

Publisher

Bentham Science Publishers Ltd.

Subject

Radiology, Nuclear Medicine and imaging

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GAN‐MD: A myocarditis detection using multi‐channel convolutional neural networks and generative adversarial network‐based data augmentation;CAAI Transactions on Intelligence Technology;2024-03-14

2. Research on Acoustic Environment Design of Residential Communities based on genetic Algorithm;2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON);2023-12-29

3. Application of Artificial Intelligence Technology in COVID-19 Imaging;Advances in Clinical Medicine;2023

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