Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays.
Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 727 - 747 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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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=184081721&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081721 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081721 184081721 184081721 10.1007/s10278-024-01201-y 184081721 ppf: 727 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays. aug: au: Vyas, Rupali Khadatkar, Deepak Rao affil: Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, C.G, India sug: subj: Deep Learning Utilization Machine Learning Utilization Pneumonia Classification Radiography, Thoracic Pneumonia Radiography Human Models, Statistical Neural Networks (Computer) Logistic Regression Support Vector Machine Decision Trees Random Forest Sensitivity and Specificity Descriptive Statistics Patient Care Decision Making Radiographic Image Interpretation, Computer-Assisted ab: Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We deployed modified VGG19, ResNet50V2, and DenseNet121 models for feature extraction, followed by five machine learning classifiers (logistic regression, support vector machine, decision tree, random forest, artificial neural network). The approach we suggested displayed remarkable accuracy, with VGG19 and DenseNet121 models obtaining 99.98% accuracy when combined with random forest or decision tree classifiers. ResNet50V2 achieved 99.25% accuracy with random forest. These results illustrate the advantages of merging deep learning models with machine learning classifiers in boosting the speedy and accurate identification of pneumonia. The study underlines the potential of DLxMLC systems in enhancing diagnostic accuracy and efficiency. By integrating these models into clinical practice, healthcare practitioners could greatly boost patient care and results. Future research should focus on refining these models and exploring their application to other medical imaging tasks, as well as including explainability methodologies to better understand their decision-making processes and build trust in their clinical use. This technique promises promising breakthroughs in medical imaging and patient management. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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