COVID-19 Detection using Transfer Learning with Convolutional Neural Network

Dutta, Pramit; Roy, Tanny; Anjum, Nafisa

doi:10.1109/icrest51555.2021.9331029

Cited by 24 publications

(6 citation statements)

References 7 publications

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“…Yener and Oktay proposed [53] in 2020 that they employed VGG16, VGG19, and Xception for COVID-19 detection using CT scan pictures, and that VGG16 had the best results with a learning rate of 10-4, accuracy of 0.91, and precision of 0. [60], with an accuracy of 84 percent. Amran et al proposed [61] in 2021 utilising CNN and variations of U-Net to distinguish be-tween COVID-19 and CAP, with an accuracy of 87.9 and sensitivity of 90.7.…”

Section: Methods For Deep Learning Techniquementioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

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COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with.

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Section: Methods For Deep Learning Techniquementioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

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“…Yang et al [16] used a 3D-ResNet deep learning system based on CT data of patients with community-acquired pneumonia (CAP) and COVID-19, finding it provided disease detection and identification with 88.8% accuracy. Dutta et al [17] developed two algorithms, inception-V3 for deep learning and a deep neural network (DNN), that take information from patient chest CT scans as a database for detecting COVID-19 infection and provide 84% accuracy. Afshar et al [18] designed a deep learning-based capsule networking system that differentiates between COVID-19 and CAP patients, distinguishing between infected and non-infected cases using a random forest (RF) classifier with an absolute accuracy of 90.8%.…”

Section: Introductionmentioning

confidence: 99%

“…Dutta et al. [17] developed two algorithms, inception‐V3 for deep learning and a deep neural network (DNN), that take information from patient chest CT scans as a database for detecting COVID‐19 infection and provide 84% accuracy. Afshar et al.…”

Section: Introductionmentioning

confidence: 99%

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

Sameer

Gharghan

Mutlag

2023

The Journal of Engineering

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The 2019 coronavirus disease began in Wuhan, China, and spread worldwide. This pandemic was concerning, given its significant and worrying impact on human health. Strategies to manage the disease begin with diagnosing the infection, often using the real-time reverse transcription polymerase chain reaction (RT-PCR) assay. However, this process is time intensive. Therefore, alternative rapid methods to diagnose the coronavirus with high accuracy are needed. X-ray and computerized tomography (CT) scans are reasonable solutions for rapid coronavirus diagnosis. The dataset of 500 patients was tested, including 286 uninfected patients and 214 infected with COVID-19. Clinical parameters, including heart rate (HR), temperature (T), blood oxygen level, D-dimer, and CT scan, including red-green-blue (RGB) pixel values of the left and right lungs, were collected from 500 patients and used to train an artificial neural network (ANN) to diagnose coronavirus. The ANN was hybridized with a particle swarm optimization (PSO) algorithm to improve diagnosis accuracy. The results show that the proposed PSO-ANN method significantly improved diagnosis accuracy (98.93%), sensitivity (100%), and specificity (98.13%). The effectiveness of the proposed method was confirmed by comparing the findings with those of previous studies.

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“…(a) The large number of learning parameters are constraints to implement deep learning models in real time applications. (b) The most of the work done in the realm of covid‐19 detection [ 6 , 7 , 8 , 9 ] are based on transfer learning due to unavailability of large number of dataset.…”

Section: Introductionmentioning

confidence: 99%

LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images

Ahmed

Tan

2022

IET Image Processing

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Coronavirus Disease 2019 (Covid‐19) overtook the worldwide in early 2020, placing the world's health in threat. Automated lung infection detection using Chest X‐ray images has a ton of potential for enhancing the traditional covid‐19 treatment strategy. However, there are several challenges to detect infected regions from Chest X‐ray images, including significant variance in infected features similar spatial characteristics, multi‐scale variations in texture shapes and sizes of infected regions. Moreover, high parameters with transfer learning are also a constraints to deploy deep convolutional neural network(CNN) models in real time environment. A novel covid‐19 lightweight CNN(LW‐CovidNet) method is proposed to automatically detect covid‐19 infected regions from Chest X‐ray images to address these challenges. In our proposed hybrid method of integrating Standard and Depth‐wise Separable convolutions are used to aggregate the high level features and also compensate the information loss by increasing the Receptive Field of the model. The detection boundaries of disease regions representations are then enhanced via an Edge‐Attention method by applying heatmaps for accurate detection of disease regions. Extensive experiments indicate that the proposed LW‐CovidNet surpasses most cutting‐edge detection methods and also contributes to the advancement of state‐of‐the‐art performance. It is envisaged that with reliable accuracy, this method can be introduced for clinical practices in the future.

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COVID-19 Detection using Transfer Learning with Convolutional Neural Network

Cited by 24 publications

References 7 publications

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters

LW‐CovidNet: Automatic covid‐19 lung infection detection from chest X‐ray images

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