Classification of Brain Tumor by Combination of Pre-Trained VGG16 CNN

Document Type: Proceedings of The 6'th International Conference on Communication Management and Information Technology (ICCMIT'20)


1 Laboratoire de la Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, BP 68M, 16309, Oued-Smar, Alger, Algérie.

2 Laboratoire de la Communication dans les Systèmes Informatiques (LCSI), École Nationale Supérieure d’Informatique (ESI), BP 68M, 16309, Oued-Smar, Alger, Algérie.


In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). The scope of this research is the use of gray level of co-occurrence matrix (GLCM) features images and the original images as inputs to CNNs. Two GLCM features images are used (contrast and energy image). Our experiments show that the original image with energy image as input has better distinguishing features than other input combinations; accuracy can achieve average of 96.5% which is higher than accuracy in state-of-the-art classifiers.


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