Superlative Feature Selection Based Image Classification Using Deep Learning in Medical Imaging.
Journal
Journal of healthcare engineering
ISSN: 2040-2309
Titre abrégé: J Healthc Eng
Pays: England
ID NLM: 101528166
Informations de publication
Date de publication:
2022
2022
Historique:
received:
09
08
2022
revised:
06
09
2022
accepted:
17
09
2022
entrez:
6
10
2022
pubmed:
7
10
2022
medline:
12
10
2022
Statut:
epublish
Résumé
Medical image recognition plays an essential role in the forecasting and early identification of serious diseases in the field of identification. Medical pictures are essential to a patient's health record since they may be used to control, manage, and treat illnesses. On the other hand, image categorization is a difficult problem in diagnostics. This paper provides an enhanced classifier based on the outstanding Feature Selection oriented Clinical Classifier using the Deep Learning (DL) model, which incorporates preprocessing, extraction of features, and classifying. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The proposed methodology is based on feature extraction with the pretrained EfficientNetB0 model. The optimum features enhanced the classifier performance and raised the precision, recall, F1 score, accuracy, and detection of medical pictures to improve the effectiveness of the DL classifier. The paper aims to develop an optimum feature extraction model for successful medical imaging categorization. The optimum features enhanced the classifier performance and raised the result parameters for detecting medical pictures to improve the effectiveness of the DL classifier. Experiment findings reveal that our presented approach outperforms and achieves 98% accuracy.
Identifiants
pubmed: 36199372
doi: 10.1155/2022/7028717
pmc: PMC9529489
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
7028717Informations de copyright
Copyright © 2022 Mamoona Humayun et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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