Review Paper for Detection of COVID-19 from Medical Images and/ or Symptoms of Patient using Machine Learning Approaches

Siddhu, Akshay Kumar; Kumar, Ashok; Kundu, Shakti

doi:10.1109/smart50582.2020.9336799

Cited by 15 publications

(3 citation statements)

References 10 publications

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“…A review study ( Siddhu et al, 2020 ) presents a database of X-Ray and CT-Scan images from patients with common bacterial pneumonia, Covid19 disease, and common cases to detect Coronavirus infection. The authors ( Siddhu et al, 2020 ) present the conventional Deep Learning techniques for automated COVID19 patient identification in a table. They counted the number of Deep learning (DL) Architectures, analyses (such as Classification techniques), and datasets used to find COVID19.…”

Section: Literature Reviewmentioning

confidence: 99%

Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images

A-Alam

Khan

Nasir

2023

Intelligent Systems with Applications

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Section: Literature Reviewmentioning

confidence: 99%

Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images

A-Alam

Khan

Nasir

2023

Intelligent Systems with Applications

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“…Some of the Researchers made a review about choosing the most effective DL models to identify the lung segment and predict COVID-19 patients. This review paper [4] used DL networks to do a thorough analysis of the completed studies of COVID-19 diagnosis. In one paper [5], they proposed solutions of using Chest radiographs with different deep learning CNN architectures for the goal of feature extraction.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of COVID-19 Using Deep Learning

2023

ijfans

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The world health organization states that the coronavirus epidemic has created a daily threat to the global healthcare system. After numerous deaths around the world, the pandemic unlocked a new threat making people ready for something which is similar and unpredictable. There were many challenges including the shortage of medical staff, beds, diagnosis centres, and intensive care units. Correct detection of disease is also crucial in surviving the pandemic. So, with a growing need for accurate and rapid diagnosis, there are many alternatives that are derived to identify the disease with the help of Radiology and Computed Tomography (CT) scans. This paper proposes a deep-learning-based approach for the detection of COVID-19 from X-ray and CT-scan images and is based on Predefined CNN architectures such as DenseNet201 and ResNet152, which are fine-tuned to classify images as COVID-19 positive or negative. The results obtained demonstrate that the proposed methods achieve high accuracy in detecting COVID-19 cases from X-ray and CT scan images. Hence, this project can be used as a valuable tool for frontline healthcare workers and public health officials to fight against the COVID-19 pandemic.

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“…The National Health Service (NHS), or the British National Health Service, is a system that adheres to the principle of selective relief for the poor and is dedicated to the task of ensuring publicly funded health care for all in the UK [3]. The health care system is based on primary care centers, with hospitals as a support.…”

Section: International Sub-medical Model Advantages To Learn From 31 Health System-based Hierarchical Medical Modelmentioning

confidence: 99%

The Application of “Big Data Analysis + Hierarchical Medical” Model in the Context of the COVID-19

et al. 2021

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The COVID-19 epidemic has swept the world, causing serious impact and influence on economic development and residents' life in countries all over the world. This paper takes China as an example, further analyses the characteristics of China's hierarchical medical model based on the international hierarchical medical research planning, and proposes the application of “big data analysis + hierarchical medical” model for the new coronavirus epidemic and other public health emergencies based on the advantages of big data application to solve public health crises, in order to provide a reference for the planning of hierarchical medical system during the epidemic. It is expected to provide reference for the planning of hierarchical medical and health system during the epidemic, which is an innovative attempt of the medical industry.

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Review Paper for Detection of COVID-19 from Medical Images and/ or Symptoms of Patient using Machine Learning Approaches

Cited by 15 publications

References 10 publications

Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images

Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images

Detection of COVID-19 Using Deep Learning

The Application of “Big Data Analysis + Hierarchical Medical” Model in the Context of the COVID-19

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