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    Classification of Brain Tumors using Convolutional Neural Network from MR Images
    (IEEE, 2020) Gungen, Cahfer; Polat, Ozlem; Karakis, Rukiye
    The classification of brain tumors has great importance in medical applications that benefit from computer-assisted diagnosis. Misdiagnosis of brain tumor types, both prevents the patient's response to treatment effectively and reduce the chance of survival. This study proposes a solution for the classification of brain tumors using MR images. The most common brain tumors, glioma, meningioma and pituitary, are detected using convolutional neural networks. The convolutional network is trained and tested on an accessible Figshare dataset containing 3064 MR images using four different optimizers. AUC, sensitivity, specificity and accuracy are used as performance measure. The proposed method is comparable to the literature and classifies brain tumors with an average accuracy of 96.84% and a maximum accuracy of 97.75%.