MRI Brain Tumor Classification Using a Hybrid VGG16-NADE Model

Abstract

Abstract A brain tumour is determined to be abnormal cell development on the brain walls and inside the skull. A malignant variation is a dangerous form of cancer with an increased mortality rate. Analyzing Magnetic Resonance Imaging (MRI) through deep learning models is the most prevalent and accurate method of early cancer detection. A novel hybrid model is proposed with the VGG16 convolution neural network (CNN) and Neural Autoregressive Distribution Estimation (NADE). The experiment was conducted on 3064 MRI brain tumour images grouped into three categories. The T1 weighted contrast-enhanced MRI images were classified using the hybrid VGG16-NADE model and compared with other methods. The results prove that the proposed hybrid VGG16-NADEmodel outperforms the rest in terms of classification accuracy, specificity, sensitivity and F1 score. The prediction accuracy of the proposed hybrid VGG16-NADE is 96.01%, precision 95.72%, recall 95.64%, F-measure 95.68%, Receiver operating characteristic (ROC) 0.91, error rate 0.075, and the Matthews correlation coefficient (MCC) 0.3564. The numerical outcomes are comparatively higher than those from other approaches and it is evaluated with existing approaches like the hybrid CNN and NADE, CNN, CNN- kernel Extreme Learning Machines (KELM), deep CNN-data augmentation, and CNN- Genetic Algorithm (GA). Other metrics like the p-value, MCC, error rate and ROC are also evaluated. The experimental outcomes show that the hybrid VGG16-NADE classifier model outperforms other approaches.

Keywords

• MRI
• brain tumor
• VGG16
• NADE model
• classification
• deep learning.