The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric.
Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
7/21/2022
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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=158121108&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158121108 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 7/21/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158121108 158121108 158121108 10.1155/2022/5260231 158121108 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric. aug: au: Alshamrani, Khalaf Alshamrani, Hassan A. Asiri, Abdullah A. Alqahtani, F. F. Mohammad, Walid Theib Alshehri, Ali H. affil: Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Saudi Arabia sug: subj: Pneumonia Radiography Radiography, Thoracic Machine Learning Utilization Prediction Models Evaluation Image Processing, Computer Assisted Human Child Neural Networks (Computer) Predictive Value of Tests Pneumonia Classification Validity Sensitivity and Specificity Child: 6-12 years ab: Pneumonia is a common lung disease that is the leading cause of death worldwide. It primarily affects children, accounting for 18% of all deaths in children under the age of five, the elderly, and patients with other diseases. There is a variety of imaging diagnosis techniques available today. While many of them are becoming more accurate, chest radiographs are still the most common method for detecting pulmonary infections due to cost and speed. A convolutional neural network (CNN) model has been developed to classify chest X-rays in JPEG format into normal, bacterial pneumonia, and viral pneumonia. The model was trained using data from an open Kaggle database. The data augmentation technique was used to improve the model's performance. A web application built with NextJS and hosted on AWS has also been designed. The model that was optimized using the data augmentation technique had slightly better precision than the original model. This model was used to create a web application that can process an image and provide a prediction to the user. A classification model was developed that generates a prediction with 78 percent accuracy. The precision of this calculation could be improved by increasing the epoch, among other subjects. With the help of artificial intelligence, this research study was aimed at demonstrating to the general public that deep-learning models can be created to assist health professionals in the early detection of pneumonia. 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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