Classification of brain tumor image based on High Grade and Low Grade using CNN with LSTM - S. Rukmani Devi , Dr. P.Selvaraju (International Journal of Advanced Science and Technology, 2020)
Magnetic resonance imaging (MRI) is a normally available imaging method for theexamination of these cancers or tumors, but the vast amount of data provided by MRI prohibitswithin a reasonable time restricting by manual segmentation based on the procedure of precisequantifiable measurements in medical practice. Gliomas are the most prevalent and destructivebrain tumors, resulting at its highest grade in a very small life of expectancy. In general, gliomascan be classified into High (HG) and Low (LG) Grades. The precise diagnosis of the gliomaallows to define the progression of the infection and to choose the therapeutic plan. Althoughclassification of medical images has attained remarkable success using a Convolution NeuralNetworks (CNNs), but it is dispute for finding out the medical image exactly over 3D. This paperhas proposed to evaluate the CNN with Long Short Term Memory (LSTM) for analyzing gliomaof LG and HG 3D brain tumor image. In order to classify the volume of 3D brain tumors, thepre-trained VGG-16 gets extracted and served into LSTM for learning higher level featureswhile feature extraction. Based on the results produced by this method, the best method issuggested for brain tumor identification and further, for analysis of MRI images.