Detection and Classification of Brain tumors using Deep Learning and Compressive Sensing in magnetic resonance imaging
Keywords:
Compressive sensing, Deep learning, magnetic resonance imaging, image classification, convolutional neural network, brain tumorsAbstract
In this article he presents a new methodology for the detection of brain tumors in magnetic resonance imaging. The FIGSHARE database of the University of Southern Medicine, Guangzhou, China, was used. Mathematical morphology techniques were used for image conditioning to detect the area of interest in the image. Additionally, an algorithm was developed to determine the type of slice (axial, sagittal or coronal). For this procedure, the statistical mathematical technique k-means was used. Likewise, a pattern extraction was performed from each image using compressive sensing (CS). For the detection, segmentation and classification of the tumors, Deep learning based on convolutional neuronal networks was implemented applying R-CNN (regions with convolutional networks). The results obtained in the validation stage achieved a classification and detection of brain tumors in magnetic resonance images with high percentages of accuracy of 87.5% and 95.2%. The data was divided into 65% for network training, 18% for the test and 17% for the validation process. Finally, by applying deep learning and compressive sensing, brain tumors were detected and classified into three different types: meningioma, glioma, and pituitary with an accuracy rate of 87.5% and 95.2%
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