X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic

Ghaderzadeh, Mustafa; Aria, Mehrad; Asadi, Farkhondeh

doi:10.1155/2021/9942873

Cited by 32 publications

(21 citation statements)

References 104 publications

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“…Preprocessing is a frequently used process in machine vision applications, directly affecting the performance of ML‐based models. Preprocessing techniques can help prepare input data for removing noises, improving the diagnosis, emphasizing the useful features of the image, or even accelerating the DL model training process 24,25 . In the proposed method, four stages were used for data preprocessing: segmentation, decoding and resizing, normalization, and augmentation.…”

Section: Methodsmentioning

confidence: 99%

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Ghaderzadeh

Aria

Hosseini

et al. 2021

Int J of Intelligent Sys

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The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from noncancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, Mobile-NetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and Dense-Net201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved

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Section: Methodsmentioning

confidence: 99%

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Ghaderzadeh

Aria

Hosseini

et al. 2021

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

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“…The solutions proposed by the authors may contribute to the development of new computational models that can help develop the technical and medical sciences. Despite the enthusiastic approach of researchers and the suggestions that CXR should be a first-line diagnostic method for the detection and screening of COVID-19 cases [54], organisations such as the American College of Radiology [55], the Society of Thoracic Radiology and the American Society of Emergency Radiology [56] do not recommend routine imaging, especially CT, as a first-line diagnostic test for the detection of SARS-CoV-2 infection.…”

Section: Discussionmentioning

confidence: 99%

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review

Kufel¹,

Bargieł²,

Koźlik³

et al. 2022

Int. J. Med. Sci.

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This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results:The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

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“…In addition, a transfer learning method was also applied for binary and multi-class classification of Xray images. The authors of [3] investigated X-rays equipped with AI (Artificial Intelligence), supplying improved results compared to the ones without AI. Mehdi Yousefzadeh et al [4] introduced ai-corona, a deep learning framework for Covid-19 detection using CT-scan images.…”

Section: Literature Reviewmentioning

confidence: 99%

X-Ray Image Classification for COVID-19 Using Transfer Learning

Abhishek

Ranjan

Sahni

2023

Advances in Transdisciplinary Engineering

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SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it’s not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It’ll make it a lot easier to determine who’s Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%.

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X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic

Cited by 32 publications

References 104 publications

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review

X-Ray Image Classification for COVID-19 Using Transfer Learning

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