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http://dx.doi.org/10.9708/jksci.2021.26.07.037

Deep Learning-Based Brain Tumor Classification in MRI images using Ensemble of Deep Features  

Kang, Jaeyong (Dept. of Software, Korea National University of Transportation)
Gwak, Jeonghwan (Dept. of Software, Korea National University of Transportation)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.7, 2021 , pp. 37-44 More about this Journal
Abstract
Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.
Keywords
Artificial intelligence; Machine learning; Deep learning; Brain tumor classification; Transfer learning; Ensemble learning;
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