DeepJoint segmentation for the classification of severity‐levels of glioma tumour using multimodal MRI images
| Autor: | Arokia Renjit J, Michael Mahesh K |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Feature extraction 02 engineering and technology brain tumour segmentation Convolutional neural network Surgical planning surgical planning QA76.75-76.765 oedema tumours Region of interest Glioma Photography 0202 electrical engineering electronic engineering information engineering medicine glioma tumour magnetic resonance imaging Segmentation Computer software Electrical and Electronic Engineering TR1-1050 Contextual image classification business.industry 020206 networking & telecommunications Pattern recognition Image segmentation medicine.disease Signal Processing multimodal MRI images 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | IET Image Processing, Vol 14, Iss 11, Pp 2541-2552 (2020) |
| ISSN: | 1751-9667 1751-9659 |
| DOI: | 10.1049/iet-ipr.2018.6682 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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