access icon free DeepJoint segmentation for the classification of severity-levels of glioma tumour using multimodal MRI images

Brain tumour segmentation is the process of separating the tumour from normal brain tissues. A glioma is a kind of tumour, which fires up in the glial cells of the spine or the brain. This study introduces a technique for classifying the severity levels of glioma tumour using a novel segmentation algorithm, named DeepJoint segmentation and the multi-classifier. Initially, the brain images are subjected to pre-processing and the region of interest is extracted. Then, the segmentation of the pre-processed image is done using the proposed DeepJoint segmentation, which is developed through the iterative procedure of joining the grid segments. After the segmentation, feature extraction is carried out from core and oedema tumours using information-theoretic measures. Finally, the classification is done by the deep convolutional neural network (DCNN), which is trained by an optimisation algorithm, named fractional Jaya whale optimiser (FJWO). FJWO is developed by integrating the whale optimisation algorithm in fractional Jaya optimiser. The performance of the proposed FJWO–DCNN with the DeepJoint segmentation method is analysed using accuracy, true positive rate, specificity, and sensitivity. The results depicted that the proposed method produces a maximum accuracy of 96%, which indicates its superiority.

Inspec keywords: brain; optimisation; biomedical MRI; tumours; medical image processing; image classification; convolutional neural nets; feature extraction; neurophysiology; cancer; image segmentation

Other keywords: brain tumour segmentation; magnetic resonance imaging; deep convolutional neural network; glioma tumour; fractional Jaya whale optimiser; multiclassifier; oedema tumours; glial cells; normal brain tissues; surgical planning; multimodal MRI images; brain images; DeepJoint segmentation; treatment planning

Subjects: Biomedical magnetic resonance imaging and spectroscopy; Biology and medical computing; Neural computing techniques; Patient diagnostic methods and instrumentation; Medical magnetic resonance imaging and spectroscopy; Image recognition; Optimisation techniques; Computer vision and image processing techniques; Biophysics of neurophysiological processes; Optimisation techniques

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