The treatments for symptoms of Alzheimer’s disease are being provided and for the early diagnosis... more The treatments for symptoms of Alzheimer’s disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination meth...
Early diagnosis is critical for the development and success of interventions, and neuroimaging is... more Early diagnosis is critical for the development and success of interventions, and neuroimaging is one of the most promising areas for early detection of Alzheimer's disease (AD). This study is aimed to develop a deep learning method to extract valuable AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI), and cognitively normal (CN) groups. In this work, we adapted and trained convolutional neural networks (CNNs) on sMRI images of the brain from ADNI datasets available in online databases. Our proposed mechanism was used to combine features from different layers to hierarchically transform the images from magnetic resonance imaging into more compact high-level features. The proposed method has a reduced number of parameters which reduces the computation complexity. The method is compared with the existing state-of-the-art works for AD classification, which show superior results for the widely used evaluation metrics, including accuracy, an area under the ROC curve, etc., suggesting that our proposed convolution operation is suitable for the AD diagnosis INDEX TERMS Alzheimer's disease (AD), magnetic resonance imaging (MRI), convolutional neural network (CNN), image classification.
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Papers by Fazal Faisal