Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

Narin, Ali

doi:10.1109/tiptekno50054.2020.9299289

Cited by 29 publications

(5 citation statements)

References 16 publications

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“…A COVID-19 radiography dataset with chest X-ray images was used, where the system detects infected lungs through classifying the lung images. Narin et al [21] suggested a system based on multiclass classification using the ResNet model. A very low number of instances were used to train the mode, which makes the reliability of the system questionable.…”

Section: Related Workmentioning

confidence: 99%

“…Pooling: Pooling plays a significant role on feature extraction, where it pools high-level features from the image, such as edge and pixel data, to be processed. Max pooling, average pooling and sum pooling are the three types of pooling techniques that are mostly used [21].…”

Section: Microscopic Description Of Cnn Architecturementioning

confidence: 99%

“…GAN is used for generating synthetic images with an adversarial network form. To develop the image classification-based system, CNN is mostly used, where different researchers using CNN algorithms in their work [16][17][18][19][20][21][22]. One of the major facts of achieving improved accuracy is model selection.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling

et al. 2023

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COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.

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Section: Related Workmentioning

confidence: 99%

Section: Microscopic Description Of Cnn Architecturementioning

confidence: 99%

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A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling

et al. 2023

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“…Recall or True Positive Rate = TP TP + FN (13) where FP, FN, TP, and TN are false-positive, false-negative, true-positive, and true-negative values, correspondingly. The F1 score is used to measure the model's accuracy, and it can be computed using Equation ( 4):…”

Section: Evaluation Metricsmentioning

confidence: 99%

Hybrid Deep Convolutional Generative Adversarial Network (DCGAN) and Xtreme Gradient Boost for X-ray Image Augmentation and Detection

Basori,

Malebary,

Alesawi

2023

Applied Sciences

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The COVID-19 pandemic has exerted a widespread influence on a global scale, leading numerous nations to prepare for the endemicity of COVID-19. The polymerase chain reaction (PCR) swab test has emerged as the prevailing technique for identifying viral infections within the current pandemic. Following this, the application of chest X-ray imaging in individuals provides an alternate approach for evaluating the existence of viral infection. However, it is imperative to further boost the quality of collected chest pictures via additional data augmentation. The aim of this paper is to provide a technique for the automated analysis of X-ray pictures using server processing with a deep convolutional generative adversarial network (DCGAN). The proposed methodology aims to improve the overall image quality of X-ray scans. The integration of deep learning with Xtreme Gradient Boosting in the DCGAN technique aims to improve the quality of X-ray pictures processed on the server. The training model employed in this work is based on the Inception V3 learning model, which is combined with XGradient Boost. The results obtained from the training procedure were quite interesting: the training model had an accuracy rate of 98.86%, a sensitivity score of 99.1%, and a recall rate of 98.7%.

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“…After receiving an image, the doctor uploads it to the app, which uses texture and shape descriptors or classical classifiers for feature extraction and makes analysis by the intelligent system to identify COVID-19. Similarly, Narin [ 27 ] uses the ResNet-50 model of convolutional neural network (CNN) to carry out diagnostic research. With the help of the supervised learning method based on statistical learning theory (SVM algorithm), features can be also directly extracted to determine whether the disease is present [ 32 ].…”

Section: Intelligent Diagnosis Of Covid-19mentioning

confidence: 99%

AIoT Used for COVID-19 Pandemic Prevention and Control

Chen

Wang

et al. 2021

Contrast Media & Molecular Imaging

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The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future.

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Detection of Covid-19 Patients with Convolutional Neural Network Based Features on Multi-class X-ray Chest Images

Cited by 29 publications

References 16 publications

A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling

A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling

Hybrid Deep Convolutional Generative Adversarial Network (DCGAN) and Xtreme Gradient Boost for X-ray Image Augmentation and Detection

AIoT Used for COVID-19 Pandemic Prevention and Control

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