Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals
| Autor: | Ali Biçer, Emre Dandil |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
medicine.diagnostic_test Contextual image classification Receiver operating characteristic business.industry Spectral entropy 020206 networking & telecommunications Magnetic resonance imaging 02 engineering and technology TUMOUR DETECTION Signal Processing 0202 electrical engineering electronic engineering information engineering Research studies Medicine 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering business Nuclear medicine Grading (tumors) Software |
| Zdroj: | IET Image Processing. 14:1967-1979 |
| ISSN: | 1751-9667 1751-9659 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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