COVID-19 Identification from Chest X-Rays

Mporas, Iosif; Naronglerdrit, Prasitthichai

doi:10.1109/bia50171.2020.9244509

Cited by 23 publications

(4 citation statements)

References 22 publications

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“…Ensemble deep neural network architectures are generally applied for the optimization of the performance of a single constructed network. Mporas and Naronglerdrit [21] evaluated the existing well-known pretrained models identification of COVID-19 in X-Ray images with publicly available datasets. Rafi [22] implemented an ensemble system for the detection of COVID-19 in X-Ray images.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

Sanagavarapu

Sridhar

Gopal

2021

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

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The occurrence of imbalanced datasets in medical imaging has proven to be a challenge for the development of models to analyze and evaluate the underlying condition. In this paper, the bias of the chest CT scan dataset is handled by taking discrete splits and employing ResNets to detect COVID-19 in each split. The scraped images were pre-processed using CLAHE histogram for comparison with low contrast images. Multiple ResNets were extended to form an ensemble neural network model using ANNs which handles the class imbalance. The system has an overall accuracy of 87.23% and the performance is assessed for each class. The image features identified are visualized using the GradCAM algorithm and some of the commonly found clinical features in the CT scan images of the patients suffering from this disease are summarized for better understanding the working of the model.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

Sanagavarapu

Sridhar

Gopal

2021

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

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“…Erwin Halim [17] applied on the different models for early detection of breast cancer like DWT-based multi-resolution MRF (MMRF) segmentation for mammography, MLP for histologic examination, and k--SVMRFE method for gene identification.he expected to increase the accuracy in early detection of breast cancer. Hanij et al [18] work with thermal image. Use LSVM and CNN for processing the images.…”

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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“…It is endorsed as an epidemic by WHO after it had spread to over 100 nations and caused countless cases. [1]. Fever, sore throat, headache, cough, and moderate respiratory symptoms can all be signs of coronavirus infection, which can even develop into pneumonia.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection from Chest Radiograph Images of Patients using Neural Networks

Idupulapaati

Sreedevi

2022

2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)

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The fast escalation of COVID-19 since its outbreak has affected the health care system globally. To pare the dissemination of the disease it is important to detect COVID-19 positive cases at an early stage and isolate the patients as early as possible. So, an effective and timely method for detecting the disease is required. Despite the fact that RT-PCR is the benchmark for COVID-19 detection and is very accurate, it is time-consuming. Applying deep learning along with radiography may help in diagnosing patients with COVID-19 much fast. In the paper, we propose a deep learning-based CNN architecture inspired by AlexNet which takes a three-channel image input.

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COVID-19 Identification from Chest X-Rays

Cited by 23 publications

References 22 publications

COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

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

COVID-19 Detection from Chest Radiograph Images of Patients using Neural Networks

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