Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

Popis výsledku v původním jazyce

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%.

Název v anglickém jazyce

Gated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification

Popis výsledku anglicky

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%.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

58982-58993

Kód UT WoS článku

001017320600001

EID výsledku v databázi Scopus

2-s2.0-85161612733