MRS-XNet: An Explainable One-Dimensional Deep Neural Network for Magnetic Spectroscopic Data Classification

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Recently, Computer Aided Diagnosis (CAD) systems based on Convolutional Neural Networks (CNNs) have shown promising performance in Alzheimer’s Disease (AD) diagnosis using imaging biomarkers. However, the detection of early AD stages namely Mild Cognitive Impairment (MCI) and Mild Alzheimer Disease (MAD) remains hard to achieve using imaging features. Proton Magnetic Resonance Spectroscopy ( <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> H-MRS), a powerful non-invasive technique for early diseases diagnosis, provides a promising solution for early biological brain changes detection. In this paper, we propose an explainable classification framework for early AD detection using <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> H-MRS. The proposed method consists of an end-to-end One Dimensional-CNN model integrating a novel decision interpretation method. Data used in this paper are collected in the University Hospital of Poitiers, which contain 111 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> H-MRS samples divided into 3 classes namely Normal Control (NC), MCI and MAD. The proposed framework achieves an accuracy of 82% between for the most challenging classification task (MCI vs. MAD classification). Yet, the proposed AD detection framework is explainable, highlighting the clinically relevant brain metabolites for early AD subjects discrimination.