Brain Tumor Classification Using Convolutional Neural Network

Abstract: Medical image classification has gained tremendous attention in recent years, and Convolutional Neural Network (CNN) is the most widespread neural network model for image classification problem. CNN is designed to determine features adaptively through backpropagation by applying numerous building blocks, such as convolution layers, pooling layers, and fully connected layers. In this paper, we mainly focused on developing a CNN model for classifying brain tumors in T1-weighted contrast- enhanced MRI images. The proposed system consists of two significant steps. First, preprocess the images using different image processing techniques and then classify the preprocessed image using CNN. The experiment is conducted on a dataset of 3064 images which contain three types of brain tumor (glioma, meningioma, pituitary). We achieved a high testing accuracy of 94.39%, average precision of 93.33% and an average recall of 93% using our CNN model. The proposed system exhibited satisfying accuracy on the dataset and outperformed many of the prominent existing methods.

Recommended citation: S. Das, O. F. M. R. R. Aranya and N. N. Labiba, "Brain Tumor Classification Using Convolutional Neural Network," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-5, doi: 10.1109/ICASERT.2019.8934603.
Download Paper | Download Slides