Two Parallel CNN Blocks for brain tumors classification

The main challenge in Magnetic Resonance Imaging (MRI) image classification is the semantic mismatch between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. Deep learning is very powerful to represent features, which can completely describe low-level and high-level information. In literature, several studies are based on the CNN networks formed by a single block; some others use architectures with multimodal technique or transfer learning approach. In this paper, we propose a deep learning technique based on a combination of two CNN blocks operating in parallel and a classification step. In the first block, deep features are extracted using small size filters (3, 3) used in all layers of this block. In the second one, shallow features are extracted using large filters of different sizes. To perform a study according to the size of different filters on the second block, we implement and evaluate six variants of this model in order to classify four types of MRI images (healthy, Meningioma, Glioma and Pituitary tumor). The experimental study confirms that the two parallel CNN blocks extract distinct features. The obtained results in terms of accuracy, precision and F1-score show that the six variants outperform the pre-trained VGG-16 model, the variant M3 of our model, on which the second block contains four convolutional layers of filters size (13,13), (11,11), (7,7), (5,5) is the best one, it reaches classification and prediction accuracy of 99.01% with a loss of 0.055.