NDLI: DeepJoint segmentation for the classification of severity-levels of glioma tumour using multimodal MRI images

Content Provider	IET Digital Library
Author	K, Michael Mahesh J, Arokia Renjit
Abstract	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.
Starting Page	2541
Ending Page	2552
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 11, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6682
Journal	IET Image Processing
Publisher Date	2020-04-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Biophysics of Neurophysiological Processes Brain Brain Image Brain Tumour Segmentation Cancer Computer Vision And Image Processing Technique Convolutional Neural Nets Deep Convolutional Neural Network DeepJoint Segmentation Feature Extraction Fractional Jaya Whale Optimiser Glial Cell Glioma Tumour Image Classification Image Recognition Image Segmentation Magnetic Resonance Imaging Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Multiclassifier Multimodal MRI Image Neural Computing Technique Neurophysiology Normal Brain Tissues Oedema Tumours Optimisation Optimisation Technique Patient Diagnostic Method And Instrumentation Spectroscopy Surgical Planning Treatment Planning Tumours
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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