2026-04-14T07:25:31Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/180262024-05-22T09:50:24Zcom_2072_452955com_2072_2054col_2072_452957

00925njm 22002777a 4500 dc Khawaldeh, Saed author Pervaiz, Usama author Rafiq, Azhar author Alkhawaldeh, Rami S. author 2017-12-25 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% http://hdl.handle.net/10256/18026 Imatge -- Segmentació Imaging segmentation Cervell -- Imatgeria per ressonància magnètica Brain -- Magnetic resonance imaging Imatgeria mèdica Imaging systems in medicine Cervell -- Tumors Brain -- Tumors Glioblastoma multiforme Glioblastoma multiforme Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks