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

Authors: Biswaranjan Mishra; Kakita Murali Gopal; Bijay Kumar Paikaray; Srikant Patnaik

Addresses: GIET University, Odisha 765022, India ' GIET University, Odisha 765022, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University, Odisha, India ' Interscience Institute of Management and Technology, Odisha 752054, India

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%.

Keywords: tumour diagnosis; optimal features; stacked autoencoder; SAE; attention block; long short-term memory; LSTM.

DOI: 10.1504/IJBRA.2024.140006

International Journal of Bioinformatics Research and Applications, 2024 Vol.20 No.3, pp.229 - 243

Received: 10 Apr 2023
Accepted: 28 Jun 2023
Published online: 15 Jul 2024 *

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