Deep Learning Techniques for the Real Time Detection of Covid19 and Pneumonia using Chest Radiographs

Panwar, Avnish; Yadav, Rishika; Mishra, Kishor; Gupta, Sujata

doi:10.1109/eurocon52738.2021.9535604

Cited by 46 publications

(5 citation statements)

References 14 publications

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“…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. Then those images were used as the input to multiple machine learning classifiers with an accuracy of 88.8%.…”

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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Section: Literature Surveymentioning

confidence: 99%

Detection of COVID-19 Using Deep Learning

2023

ijfans

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“…Once images are collected, several preprocessing techniques, including image stacking and resizing, are used to generate standardized images Initially, the size and shape of the images vary, requiring uniformity for integration in deep learning models. Resizing ensures that all images share the same resolution, facilitating seamless interaction the Mode between images throughout the analysis [18].…”

Section: Fig-3 X-ray For Trainingmentioning

confidence: 99%

A Comprehensive Approach to COVID-19 Detection and Management Using Deep Learning--Review

Tarun

2023

Int. J. Res. Publ. Rev.

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COVID-19 is a critical global health emergency that has affected millions of people worldwide. Early detection of the virus is essential to prevent its spread and death. The predominant clinical approach for detecting the presence of the covid-19 is through reverse transcription polymerase chain reaction (RT-PCR). Still, it has disadvantages, such as a high cost and a long turnaround time. In this study, different models are tested. These examples were circumstantial. CNN is a great choice for image discovery and consumption projects. Impressive results were obtained, especially in applications dealing with image data. This report focuses on enhancing the capability of Convolutional Neural Networks (CNNs) to identify COVID-19 in chest X-ray images.

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“…One particular model deals [13] with a classification challenge that involves determining if a chest X-ray reveals alterations compatible with pneumonia or not, and then categorizing the X-ray images into two categories based on the detection findings. [14] For feature extraction, chest radiographs are considered input and influence in several convolution network architectures. After the extraction process, images were apportioned into various machinelearning classifiers, which could capture the pneumonia-affected lungs or standard.…”

Section: Related Workmentioning

confidence: 99%

Detection of Post COVID-Pneumonia Using Histogram Equalization, CLAHE Deep Learning Techniques

Rajkumar,

Kalyan Reddy

et al. 2023

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Pneumonia, also known as bronchitis, is caused by bacteria, viruses, or fungi. Pneumonia can be fatal to an infected person because the lungs cannot exchange air. The disease primarily affects infants and people over the age of 65. Every year, nearly 4 million people are killed by the disease, which affects an estimated 420 million people. It is critical to detect and diagnose this condition as soon as possible. Diagnosing the condition using the patient's x-ray is the most effective method. Experienced radiologists will use a chest x-ray of the affected patient to make this informed decision. Recently, coronavirus is a contagious viral disease caused by the SARSCoV2 virus. This virus affects the human respiratory system. The virus also causes pneumonia (COVID pneumonia), which is far more dangerous than normal pneumonia. The main purpose of this task is to study and compare several deep learning enhancement techniques applied to medical x-ray and CT scan images for the detection of COVID19 (pneumonia). A convolutional neural network (CNN) is used to design a model that can distinguish between COVID19 pneumonia and normal pneumonia. In addition, image enhancement techniques (histogram equalization (HE), contrast-limited adaptive histogram equalization (CLAHE)) have been processed against the dataset to find more efficient methods and models for detecting pneumonia. A dataset of 6432 CXRs were used - 576 COVID pneumonia CXRs, 1583 normal pneumonia CXRs, and 4273 healthy lung CXRs. Based on the results, it was observed that the equalized histogram and the equalized dataset of CLAHE run faster than the original dataset. This requires a computer-aided diagnosis (CAD) system that can distinguish between COVID pneumonia, normal pneumonia, and healthy lungs. In addition, the improved VGG16 achieved 96% accuracy in the detection of X-ray images of COVID19 - pneumonia.

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Deep Learning Techniques for the Real Time Detection of Covid19 and Pneumonia using Chest Radiographs

Cited by 46 publications

References 14 publications

Detection of COVID-19 Using Deep Learning

Detection of COVID-19 Using Deep Learning

A Comprehensive Approach to COVID-19 Detection and Management Using Deep Learning--Review

Detection of Post COVID-Pneumonia Using Histogram Equalization, CLAHE Deep Learning Techniques

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