Automatic brain tumor detection using CNN transfer learning approach

Automatic brain tumor detection is a challenging task as tumors vary in their position, mass, nature, and similarities found between brain lesions and normal tissues. The tumor detection is vital and urgent as it is related to the lifespan of the affected person. Medical experts commonly utilize advanced imaging practices such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound images to decide the presence of abnormal tissues. It is a very time-consuming task to extract the tumor information from the enormous quantity of information produced by MRI volumetric data examination using a manual approach. In manual tumor detection, precise identification of tumor along with its details is a complex task. Henceforth, reliable and automatic detection systems are vital. In this paper, convolutional neural network based automated brain tumor recognition approach is proposed to analyze the MRI images and classify them into tumorous and non-tumorous classes. Various convolutional neutral network architectures like Alexnet, VGG-16, GooGLeNet, and RNN are explored and compared together. The paper focuses on the tuning of the hyperparameters for the two architectures namely Alexnet and VGG-16. Exploratory results on BRATS 2013, BRATS 2015, and OPEN I dataset with 621 images confirmed that the accuracy of 98.67% is achieved using CNN Alexnet for automatic detection of brain tumors while testing on 125 images.