IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION

IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION

Accurate and reliable diagnosis is critical for effective treatment planning for brain cancer. Recent advancements in deep learning have significantly enhanced diagnostic capabilities, but challenges persist in optimizing model performance for diverse and complex datasets. This study investigates th...

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Bibliographic Details
Main Authors:	M Muthulekshmi, Azath Mubarakali, Y M Blessy
Format:	Article
Language:	English
Published:	XLESCIENCE 2024-12-01
Series:	International Journal of Advances in Signal and Image Sciences
Subjects:	computer-aided diagnosis, brain cancer, deep learning, convolutional neural network, polyak ruppert optimization
Online Access:	https://xlescience.org/index.php/IJASIS/article/view/173
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