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

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Dandil E., Bicer A.

IET IMAGE PROCESSING, vol.14, no.10, pp.1967-1979, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 10
  • Publication Date: 2020
  • Doi Number: 10.1049/iet-ipr.2019.1416
  • Journal Name: IET IMAGE PROCESSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1967-1979
  • Keywords: magnetic resonance spectroscopy, brain, biomedical MRI, tumours, medical image processing, image classification, object detection, entropy, recurrent neural nets, malignant brain tumours, automatic grading, LSTM neural networks, magnetic resonance spectroscopy signals, brain tumour diagnosis, histopathological assessments, brain tumour detection, computer-assisted method, long short term memory neural network, spectral entropy, pattern recognition, magnetic resonance database, brain tumour classification, magnetic resonance imaging, SHORT ECHO TIME, H-1 MR SPECTROSCOPY, GLIOMA GRADE, CLASSIFICATION, DIAGNOSIS, SYSTEM, THERAPY, BIOPSY
  • Akdeniz University Affiliated: Yes

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.