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صفحه اصلی
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سی امین کنفرانس بین المللی مهندسی برق
Medial Residual Encoder Layers for Classification of Brain Tumors in Magnetic Resonance Images
نویسندگان :
Zahra Sobhaninia
1
Nader Karimi
2
Pejman Khadivi
3
Shadrokh Samavi
4
1- دانشگاه صنعتی اصفهان
2- دانشگاه صنعتی اصفهان
3- دانشگاه صنعتی اصفهان
4- دانشگاه صنعتی اصفهان
کلمات کلیدی :
Deep learning،Brain tumor classification،Image classification،Residual network
چکیده :
Correct and timely diagnosis of the brain tumor will make treatment more effective . To date, several image classification approaches have been proposed to aid diagnosis and treatment. In this work a deep learning approach has developed to increase tumor type classification accuracy in MRI images by considering medical image dataset limitation. In this regard, we offer a system based on deep learning, containing encoder blocks. In proposed approach in addition to more organized and simplicity architecture, residual approach used. encoder blocks are fed with post-max-pooling features as residual learning. Experimental evaluations of our approach shows promising results by improving the tumor classification accuracy in Magnetic resonance imaging (MRI) images using a limited medical image dataset. Experimental evaluations of this model on a dataset consisting of 3064 MR images show 95.98% accuracy, which is better than previous studies on this database.
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بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 43.6.0
