A new machine learning approach to classify MRI of brain tumour using SAE + LSTM

Abstract

Site Logo, redirects to the Home Page Search Skip main navigationOpen Drawer Menu Previous Issues Share HomeInternational Journal of Bioinformatics Research and ApplicationsVol. 20, No. 3 No Access A new machine learning approach to classify MRI of brain tumour using SAE + LSTM Biswaranjan Mishra, Kakita Murali Gopal, Bijay Kumar Paikaray and Srikant Patnaik Published Online:July 15, 2024pp 229-243https://doi.org/10.1504/IJBRA.2024.140006 About Abstract A brain tumour is a serious condition that can seriously harm brain cells and eventually progress to cancer, which is life-threatening. The patient's chances of survival can be improved when the tumour stages are detected early. The proposed tumour diagnosis uses a fused feature set to increase the classifier's accuracy. To begin with, the features from the MRI images are extracted using the grey level co-occurrence matrix (GLCM) and histogram of oriented gradients (HOG). After dimensionality reduction, features are chosen with stacked autoencoder (SAE). Second, the high-level features from the MRI images are extracted using the channel-wise attention block. The long short-term memory (LSTM) is trained to produce the results of the classification using the fused features from SAE and the attention block. The proposed approach is evaluated with the BRATS dataset for the years 2018-1020. The accuracy attained over various datasets is 97%, 95.56% and 95.23%.