Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays.
X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed...
| Publicado en: | BioMed Research International pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/23/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158121127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158121127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/23/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158121127 158121127 158121127 10.1155/2022/7833516 158121127 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays. aug: au: Badr, Malek Al-Otaibi, Shaha Alturki, Nazik Abir, Tanvir affil: The University of Mashreq, Research Center, Baghdad, Iraq sug: subj: Deep Learning Radiography, Thoracic Neural Networks (Computer) Diagnostic Imaging Lung Pathology Lung Radiography Human Data Analysis Data Analysis Software Databases Experimental Studies Transfer (Psychology) Learning Methods ab: X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks' knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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