Multi-classification of Brain Tumor Images Using Deep Neural Network

Project Code :TMMAAI27


In this paper, a DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets. The former one classifies tumors into (meningioma, glioma, and pituitary tumor). The other one differentiates between the three glioma grades (Grade II, Grade III, and Grade IV). 

Brain tumor classification is a crucial task to evaluate the tumors and make a treatment decision according to their classes. There are many imaging techniques used to detect brain tumors. However, MRI is commonly used due to its superior image quality and the fact of relying on no ionizing radiation. 

Deep learning (DL) is a subbed of machine learning and recently showed a remarkable performance, especially in classification and segmentation problems. The proposed network structure achieves a sign cant performance with the best overall accuracy of 96.13% and 98.7%, respectively, for the two studies with the ability of the model for brain tumor multi-classification purposes.

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