Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research work proposes a novel graph momentum fully convolutional network with a modified Elman spike neural network (MESNN)...

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Bibliographic Details
Published in:International journal of imaging systems and technology 2024-01, Vol.34 (1), p.n/a
Main Authors: Ramkumar, M., Kumar, R. Sarath, Padmapriya, R., Karthick, S.
Format: Article
Language:English
Subjects:
Algorithms
Brain
brain tumor segmentation
BraTS
Image segmentation
Magnetic resonance imaging
Momentum
Neural networks
overall survival prediction
radiomics features
Survival
Tumors
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Summary:Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research work proposes a novel graph momentum fully convolutional network with a modified Elman spike neural network (MESNN) for BTS and overall survival prediction (OSP). Initially, the introduced graph momentum fully convolutional network segments the brain tumor as enhanced tumor, the tumor core, and the whole tumor from the pre‐processed MRI scans. Second, the texture, intensity, shape, and wavelet features were extracted from the segmented tumors. Then, the horse herd optimization algorithm is utilized to minimize the feature's dimensionality. Finally, the OSP is performed by the MESNN which classifies the survival prediction of a patient as long‐term, mid‐term, and short‐term. The achieved segmentation accuracy of proposed method is 97% and the survival prediction's average RMSE is 215.5.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.23005