NDLI: CapsNet topology to classify tumours from brain images and comparative evaluation

Content Provider	IET Digital Library
Author	Goceri, Evgin
Abstract	Visual evaluation of many magnetic resonance images is a difficult task. Therefore, computer-assisted brain tumor classification techniques have been proposed. These techniques have several drawbacks or limitations. Capsule based neural networks are new approaches that can preserve spatial relationships of learned features using dynamic routing algorithm. By this way, not only performance of tumor recognition increases but also sampling efficiency and generalisation capability improves. Therefore, in this work, a Capsule Network (CapsNet) is used to achieve fully automated classification of tumors from brain magnetic resonance images. In this work, prevalent three types of tumors (pituitary, glioma and meningioma) have been handled. The main contributions in this paper are as follows: 1) A comprehensive review on CapsNet based methods is presented. 2) A new CapsNet topology is designed by using a Sobolev gradient-based optimisation, expectation-maximisation based dynamic routing and tumor boundary information. 3) The network topology is applied to categorise three types of brain tumors. 4) Comparative evaluations of the results obtained by other methods are performed. According to the experimental results, the proposed CapsNet based technique can achieve extraction of desired features from image data sets and provides tumor classification automatically with 92.65% accuracy.
Starting Page	882
Ending Page	889
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 5, Apr (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0312
Journal	IET Image Processing
Publisher Date	2019-12-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Brain Image Brain Magnetic Resonance Image Brain Tissues CapsNet Based Method CapsNet Topology Capsule-based Neural Network Computer Vision And Image Processing Technique Computer-assisted Brain Tumour Classification Technique Ependymoma Expectation-maximisation Algorithm Expectation-maximisation Based Dynamic Routing Feature Extraction Glioma Gradient Method Image Classification Image Recognition Learned Feature Learning in AI Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Meningioma Network Topology Neural Computing Technique Neural Nets Optimisation Optimisation Technique Patient Diagnostic Method And Instrumentation Pituitary Probability Theory Sobolev Gradient-based Optimisation Spectroscopy Statistics Stochastic Linearised SCUC Tumour Boundary Information Tumour Recognition Tumours
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals

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

AiMRS: A feature extraction method from MRS signals based on artificial immune algorithms for classification of brain tumours

Brain tumour classification using two-tier classifier with adaptive segmentation technique

Method of multi-region tumour segmentation in brain MRI images using grid-based segmentation and weighted bee swarm optimisation

Classification of magnetic resonance images for brain tumour detection

Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets

Diagnosis of brain tumours by MRI binarisation with variable fuzzy level

Kernel difference maximisation-based sparse representation for more accurate face recognition

CapsNet topology to classify tumours from brain images and comparative evaluation

Similar Documents

Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals

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

AiMRS: A feature extraction method from MRS signals based on artificial immune algorithms for classification of brain tumours

Brain tumour classification using two-tier classifier with adaptive segmentation technique

Method of multi-region tumour segmentation in brain MRI images using grid-based segmentation and weighted bee swarm optimisation

Classification of magnetic resonance images for brain tumour detection

Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets

Diagnosis of brain tumours by MRI binarisation with variable fuzzy level

Kernel difference maximisation-based sparse representation for more accurate face recognition

CapsNet topology to classify tumours from brain images and comparative evaluation