Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning

Maity, Arunit; Nair, T. R. Gopalakrishnan; Chandra, Avinash

doi:10.32628/ijsrst207614

Cited by 4 publications

(3 citation statements)

References 21 publications

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“… Most experiments do not consider the execution time problem. It is necessary to shorten the execution time with appropriate data preprocessing [ 192 , 193 , 194 , 195 ] strategies or GPU acceleration. The experiments discussed in this paper use chest CT or chest X-ray images as the input datasets of CNN and have achieved good performance.…”

Section: Discussionmentioning

confidence: 99%

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A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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“…An article in the journal IJSRST [15] proved that the model works best when using image preprocessing techniques. The preprocessing technique that produced the best results was to use global histogram smoothing.…”

Section: An Overview Of Neural Network Leverage For Medical Image Ana...mentioning

confidence: 99%

Machine learning in lung lesion detection caused by certain diseases

Khoroshchuk,

Liubinskyi

2023

Math. Model. Comput.

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On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

Okolo

Katsigiannis

Althobaiti

et al. 2021

Sensors

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The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.

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Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning

Cited by 4 publications

References 21 publications

A Survey on Deep Learning in COVID-19 Diagnosis

A Survey on Deep Learning in COVID-19 Diagnosis

Machine learning in lung lesion detection caused by certain diseases

On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

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