The use of magnetic resonance images for the detection and classification of brain cancers with D-CNN

Among the various oncological pathologies, brain cancer continues to be one of the most wide-spread, as well as lethal, diseases. Within this paper we used Keras and Tensorflow to implement state-of-the-art convolutional neural network (CNN) architectures, such as EfficientNetB0, Res-Net50 and VGG16, using Transfer Learning to detect and classify three types of brain tumors namely say – Glioma, meningioma, and pituitary. The dataset we used consisted of 3264 2-D MRI images and 4 classes. Due to the small number of images, various data augmentation techniques were used to increase the size of the dataset. Our proposed methodology consists not only of data augmentation, but also of various techniques of image denoising, skull strip-ping, cropping and bias correction. In our working proposal, the EfficientNetB0 architecture gave the best results providing a very high accuracy. The purpose of this document is to distinguish between normal and abnormal pixels and classify them more accurately.