###### Abstract  
In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%. Keywords:brain tumor classification; glioblastoma; convolutional neural network; magnetic resonance imaging +
Footnote †: journal: Medical Imaging and Applications, University of Girona, 17004 Girona, Spain; 12 [EMAIL_ADDRESS]  
Footnote †: journal: Medical Imaging and Applications, University of Girona, 17004 Girona, Spain; 12 beeupervai@seecs. with the presence of microvascular proliferation and pseudopalisading necrosis. More importantly, grading of brain tumors is crucial for determining the survival rate; e.g., Grade I has the highest overall survival, and Grade IV has the poorest overall survival. The grade of the glioma tumor during initial diagnosis and prognosis is essential to determine appropriate treatment options [5; 6]. Typically, for the initial characterization of the tumor, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are used to produce detailed images of the brain. However, for the further classification of tumors into sub-grades, a biopsy of the tumor is necessary for detailed examination by a pathologist [7]. Tissue biopsy is an invasive procedure and is time consuming, since it requires the tissue sample to be sent to a laboratory where a pathologist conducts extensive examination and classification [8]. The technology for imaging tissue has increased in resolution to identify smaller lesions causing a greater dependency on imaging for disease diagnosis. This has resulted in the use of multiple imaging technologies for cross-referencing a suspected clinical case and achieving greater accuracy in diagnosis. The consequence of more numerous imaging studies per patient is the requirement for the integration of additional technologies to aid in the diagnosis. Technologies such as computer-assisted diagnosis methods are being developed to provide supportive diagnostic tools for analyzing medical images and identifying disease, as well as grading of brain tumors [9]. An increasing number of medical imaging techniques which align with computer-based classification and segmentation algorithms are also being examined and validated by researchers. These analytic methodologies are being applied to different types of medical images for different clinical purposes including cancer tumors' staging [10]. Such innovations also address the challenge to grading tumors by human interpretation of images from multiple readers since there is the possibility of inter-reader variability when determining the tumors' grade based on the visual features of lesions. Therefore, an automated image analytic process is sought for the classification of brain tumors that would have the capacity to quantitatively assist in more objective diagnosis [11]. The task of brain tumor image analysis is very challenging with the use of traditional machine learning algorithms for glioma cancers since there are no well-defined characteristics of the tumor, and it requires accurate differentiation of the lesion from normal tissues surrounding the cancer. The efforts to utilize image analysis for cancer diagnosis are in contrast to more recent work on genetic analysis of tissue samples [12].