Crowd?Powered Face Manipulation Detection: Fusing Human Examiner Decisions

Recently, Computer Aided Diagnosis (CAD) methods based on Convolutional Neural Networks (CNNs) showed promising performance in imaging bio-markers of Alzheimer?s Disease (AD). Although identifying the transitional phases of AD namely Mild Cognitive Impairment (MCI) and Mild Alzheimer Disease (MAD) still hard to achieve. Proton Magnetic Resonance Spectroscopy (${ }^{1}$H-MRS), a powerful non-invasive technique for early diagnosis, provides a promising solution for biological brain changes detection. in this paper, we propose an explainable classification framework for early AD detection using (${ }^{1}$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 ${ }^{1}$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.