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

Content Provider	IET Digital Library
Author	Dandil, Emre Biçer, Ali
Abstract	Brain tumours have increased rapidly in recent years as in other tumour types. Therefore, early and accurate diagnosis of brain tumour is vital for treatment. Magnetic resonance imaging (MRI) and histopathological assessments are the most common methods used in the detection of brain tumours. The research studies on non-invasive imaging methods such as MRI and magnetic resonance spectroscopy (MRS) have become widespread in recent years for brain tumour detection. In this study, a computer-assisted method is proposed for automatic grading of brain tumours on MRS signals. The classification of brain tumours with different grades is performed using long short term memory (LSTM) neural networks. In addition, additional features from MRS signals based on spectral entropy and instantaneous frequency are extracted. As a result of the experimental studies on the international MRS database (INTERPRET), it is seen that grading is achieved using the proposed method with average accuracy of 98.20%, sensitivity of 100%, and specificity of 97.53% performance results in three test studies carried out for the classification of brain tumour. Furthermore, in the grading of brain tumours using the proposed method, the average area under of the receiver operating characteristic curve is measured with high performance of 0.9936.
Starting Page	1967
Ending Page	1979
Page Count	13
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1416
Journal	IET Image Processing
Publisher Date	2020-01-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Grading Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Brain Tumour Classification Brain Tumour Detection Brain Tumour Diagnosis Computer Vision And Image Processing Technique Computer-assisted Method Entropy Histopathological Assessments Image Classification Image Recognition Long Short Term Memory Neural Network LSTM Neural Network Magnetic Resonance Database Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Magnetic Resonance Spectroscopy Signal Malignant Brain Tumours Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Neural Computing Technique Object Detection Patient Diagnostic Method And Instrumentation Pattern Recognition Recurrent Neural Nets Spectral Entropy Spectroscopy Tumours
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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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
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