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 the examination of these cancers or tumors, but the vast amount of data provided by MRI prohibits within a reasonable time restricting by manual segmentation based on the procedure of precise quantifiable measurements in medical practice. Gliomas are the most prevalent and destructive brain tumors, resulting at its highest grade in a very small life of expectancy. In general, gliomas can be classified into High (HG) and Low (LG) Grades. The precise diagnosis of the glioma allows to define the progression of the infection and to choose the therapeutic plan. Although classification of medical images has attained remarkable success using a Convolution Neural Networks (CNNs), but it is dispute for finding out the medical image exactly over 3D. This paper has proposed to evaluate the CNN with Long Short Term Memory (LSTM) for analyzing glioma of LG and HG 3D brain tumor image. In order to classify the volume of 3D brain tumors, the pre-trained VGG-16 gets extracted and served into LSTM for learning higher level features while feature extraction. Based on the results produced by this method, the best method is suggested for brain tumor identification and further, for analysis of MRI images.