Automatic Detection of COVID-19 Disease in Chest X-Ray Images using Deep Neural Networks

Khan, Mohammad Mahmudur Rahman; Sakib, Shadman; Siddique, Md. Abu Bakr; Chowdhury, M. I. Sakib; Hossain, Ziad; Aziz, Anas; Yasmin, Nowrin

doi:10.1109/r10-htc49770.2020.9357034

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…The accuracies achieved were 76% for RF, 88% for SVM, and 85% for Naive Bayes. Additionally, they want to analyse the dataset using Deep Learning methods like CNN [9] Model.…”

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 accuracies achieved were 76% for RF, 88% for SVM, and 85% for Naive Bayes. Additionally, they want to analyse the dataset using Deep Learning methods like CNN [9] Model.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of COVID-19 Using Deep Learning

2023

ijfans

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“…Transfer learning (TL) is valuable in systems like CNN with a generally restricted assortment of information. In this review, we have attempted the assignment of characterization of pictures of chest X-beams into one of the following 3 classes: COVID-19-positive, viral pneumonia, or typical [5,18]. e constructed CNNs were tested using databases such as MNIST and CIFAR-10 to see how well they performed.…”

Section: Deep Convolutional Neural Network (Dcnns)mentioning

confidence: 99%

“…e pooling layer then takes what is learned from the preceding layer and reduces the process's complexity. e completely linked layer executes the features learned from all preceding layers in the second half, resulting in the intended classified outputs [5].…”

Section: Introductionmentioning

confidence: 99%

Discriminating the Pneumonia-Positive Images from COVID-19-Positive Images Using an Integrated Convolutional Neural Network

Vetrithangam

Indira

Umar

et al. 2022

Mathematical Problems in Engineering

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One of the most pressing issues in the current COVID-19 pandemic is the early detection and diagnosis of COVID-19, as well as the precise separation of non-COVID-19 cases at the lowest possible cost and during the disease's early stages. Deep learning-based models have the potential to provide an accurate and efficient approach for the identification and diagnosis of COVID-19, with considerable increases in sensitivity, specificity, and accuracy when used in the processing of modalities. COVID-19 illness is difficult to detect and recognize since it is comparable to pneumonia. The main objective of this study is to distinguish between COVID-19-positive images and pneumonia-positive images. We have proposed an integrated convolutional neural network focused on discriminating against COVID-19-infected patients and pneumonia patients. Preprocessing is done on the image datasets. The novelty of this research work is to differentiate the COVID-19 images from the pneumonia images. It will help the medical experts in the decision-making. In order to train the model, the image is given directly as input to integrated convolutional neural network architecture; after training the model, the system is integrated with three different kinds of datasets: COVID-19 image dataset, RSNA pneumonia dataset, and a new dataset created from COVID-19 image dataset. The attainment of the system is evaluated by calculating the measures of sensitivity, specificity, precision, and accuracy, and this system produces the accuracy values of 94.04%, 97.2%, and 97.5% for the above datasets, respectively.

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“…NanKrzysztof at el [20] In this paper they worked with Breast density classification which is an essential part of breast cancer screening. They apply CNN.…”

Section: Literature Riviewmentioning

confidence: 99%

An Effective Deep Learning Approach Based On CNN to Predict COVID-19 Rapidly Using Chest Images

Shing¹,

Rana²,

Basher³

et al. 2021

IJRIAS

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In December 2019 the novel coronavirus which first appeared in Wuhan City of China spread rapidly around the world and became a pandemic. It has caused a devastating effect on daily lives, public health, and the global economy. As soon as possible we have to detect the affected patient and quickly treat them. There are no accurate automated toolkits available so the need for auxiliary diagnostic tools has increased. Modern outcomes attained using radiology imaging systems recommend that such images have salient evidence about the COVID-19 virus. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is the most common test technique currently used for COVID-19 diagnosis that is too much time-consuming. Using artificial intelligence (AI) techniques associated with radiological imaging can be helpful for the accurate detection of this disease and can also be assistive to overcome the problem of an absence of specialized doctors in remote communities. In this paper, a new model based on Convolutional Neural Network (CNN) that automatically detects COVID-19 using chest images is presented. The proposed model is designed to provide accurate diagnostics for binary classification. A computer vision is rapidly relieved day by day. During our study, we observed that most of the affected people have no common symptoms before checkup COVID-19. If the detection results are incorrect, the patient will not be able to understand that he or she has Covid-19. The proposed model is evaluated by Python libraries namely TensorFlow and Keras. In the proposed model, we got 95% accuracy as well as the detection of COVID-19 is fast.

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Automatic Detection of COVID-19 Disease in Chest X-Ray Images using Deep Neural Networks

Cited by 14 publications

References 14 publications

Detection of COVID-19 Using Deep Learning

Detection of COVID-19 Using Deep Learning

Discriminating the Pneumonia-Positive Images from COVID-19-Positive Images Using an Integrated Convolutional Neural Network

An Effective Deep Learning Approach Based On CNN to Predict COVID-19 Rapidly Using Chest Images

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