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Purpose and aim:  Detection of brain tumors plays a critical role in the treatment of patients. Before any treatment, tumor segmentation is crucial to protect healthy tissues during treatment and to destroy tumor cells. Tumor segmentation involves the detection, precise identification, and separation of tumor tissues. In this paper, we provide a deep learning method for the segmentation of brain tumors. Material and methods:  In this article, we used a convolutional neural network (CNN) to segment tumors in seven types of brain disease consisting of Glioma, Meningioma, Alzheimer's, Alzheimer's plus, Pick, Sarcoma, and Huntington. First, we used the feature-reduction-based method robust principal component analysis to find tumor location and spot in a dataset of Harvard Medical School. Then we present an architecture of the CNN method to detect brain tumors. Results:  Results are depicted based on the probability of tumor location in magnetic resonance images. Results show that the presented method provides high accuracy (96%), sensitivity (99.9%), and dice index (91%) regarding other investigations. Conclusion:  The provided unsupervised method for tumor clustering and proposed supervised architecture can be potential methods for medical uses.

Detection of brain lesion location in MRI images using convolutional neural network and robust PCA