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Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE

Hashemzehi Raheleh, Mahdavi Seyyed Javad Seyyed, Kheirabadi Maryam, Kamel Seyed Reza

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

1225--1232

A brain tumor is an abnormal growth of cells inside the skull. Malignant brain tumors are among the most dreadful types of cancer with direct consequences such as cognitive decline and poor quality of life. Analyzing magnetic resonance imaging (MRI) is a popular technique for brain tumor detection. In this paper, we use these images to train our new hybrid paradigm which consists of a neural autoregressive distribution estimation (NADE) and a convolutional neural network (CNN). We subsequently test this model with 3064 T1-weight-ed contrast-enhanced images with three types of brain tumors. The results demonstrate that the hybrid CNN-NADE has a high classification performance as regards the availability of medical images are limited.