Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI

I. Amaya-Rodriguez, L. Duran-Lopez, F. Luna-Perejon, J. Civit-Masot, J. Dominguez-Morales, S. Vicente, A. Civit, D. Cascado, A. Linares-Barranco

Abstract

Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for pre-processing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means.

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