Brain Tumor Classification using Pretrained Deep Convolutional Neural Network

Abstract

In this research deep learning convolution neural network (CNN) has been implemented for binary classification GBM brain tumor. The dataset from REMBRANDT database that comprise of 155 MRI images has been utilized in this research. The transfer learning by pretrained network namely GoogleNet and AlexNet have been conducted to classify the GBM brain tumor form normal brain. The advantage of classification by transfer learning is the manual segmentation and feature extraction were replaced by automatic procedure that reduce the error from human handcraft. The prediction result by using Googlenet pretrained network shown accuracy result 80.85% and Alexnet pretrained network obtained accuracy 93.62%. The GBM brain tumor classification by deep learning pretrained network obtained good result of accuracy and can be implemented in practical to help the medical staff for earlier diagnosis for further treatment and increasing survivor rate of patients. The manual adjustment for segmentation and feature extraction are improved by automatic classification using deep learning pretrained network is main advantage of this research. The future work we will try to implement with increasing images dataset to improve accuracy, robustness testing and prevent overfitting problem.