Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10253238" target="_blank" >RIV/61989100:27230/23:10253238 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10145063" target="_blank" >https://ieeexplore.ieee.org/document/10145063</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2023.3281546" target="_blank" >10.1109/ACCESS.2023.3281546</a>
Alternative languages
Result language
angličtina
Original language name
Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification
Original language description
One of the most complex areas of image processing is image classification, which is heavily relied upon in clinical care and educational activities. However, conventional models have reached their limits in effectiveness and require extensive time and effort to extract and choose classification variables. In addition, the large volume of medical image data being produced makes manual procedures ineffective and prone to errors. Deep learning has shown promise for many classification problems. In this study, a deep learning-based classification model is developed to decrease misclassifications and handle large amounts of data. The Adaptive Guided Bilateral Filter is used to filter images, and texture and edge attributes are gathered using the Spectral Gabor Wavelet Transform. The Black Widow Optimization method is used to choose the best features, which are then input into the Red Deer Optimization-enhanced Gated Deep Reinforcement Learning network model for classification. The brain tumor MRI dataset was used to test the model on the MATLAB platform, and the results showed an accuracy of 98.8%.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
2023
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
58982-58993
UT code for WoS article
001017320600001
EID of the result in the Scopus database
2-s2.0-85161612733