Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals. Issue 10 (18th June 2020)

• Record Type: Journal Article
• Authors: Dandil, Emre; Biçer, Ali
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 1967
• Page End: 1979
• Publication Date: 2020-06-18
• Subjects: 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; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1416
• Languages: English
• ISSNs: 1751-9659

Abstract

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.