Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification. Identification of early dementia is accomplished by an eight-layered convolutional neural network (CNN) as well as transfer learning of pretrained CNNs from ImageNet. Different instantiations of the proposed CNN architecture are tested. These are equipped with Softmax, support vector machine (SVM), linear discriminant (LD), or [Formula: see text] -nearest neighbor (KNN) classification layers, assembled as a separate classification module, which are attached to the core CNN architecture. The initial imaging data were obtained from the MRI directory of the Alzheimer’s disease neuroimaging initiative 3 (ADNI3) database. The independent testing dataset was created using image preprocessing and segmentation algorithms applied to unseen patients’ imaging data. The proposed approach demonstrates a 90.12% accuracy in distinguishing patients who are cognitively normal subjects from those who have Alzheimer’s disease (AD), and an 86.40% accuracy in detecting early mild cognitive impairment (EMCI).
Advances in neural networks and deep learning have opened a new era in medical imaging technology, health care data analysis and clinical diagnosis. This paper focuses on the classification of MRI for diagnosis of early and progressive dementia using transfer learning architectures that employ Convolutional Neural Networks-CNNs, as a base model, and fully connected layers of Softmax functions or Support Vector Machines-SVMs. The diagnostic process is based on the analysis of the neurodegenerative changes in the brain using segmented images of brain asymmetry, which has been identified as a predictive imaging source of early dementia. Results from 300 independent simulation runs on a set of four binary and one multiclass MRI classification tasks illustrate that transfer learning of CNN-based models equipped with SVM output layer is capable to produce better performing models within a few training epochs compared to commonly used transfer learning architectures that combine CNN pretrained models with fully connected Softmax layers. However, experimental findings also confirm that longer training sessions appear to compensate for the shortcomings of the fully connected Softmax layers in the long term. Diagnosis of early dementia on unseen patients' brain asymmetry MRI data reached an average accuracy of 90.25% with both transfer learning architectures, while progressive dementia was promptly diagnosed with an accuracy that reached 95.90% using a transfer learning architecture that has the SVM layer.
Diffusion Tensor Imaging (DTI) and Magnetic Resonance Imaging (MRI) techniques have gained significant popularity in the diagnosis of neurodegenerative disorders. Combining brain scans with deep learning is receiving increasing attention in medical diagnostic applications. However, deep networks can learn powerful features and perform well only when a large amount of DTI or MRI image data are available. The paper attempts to reduce the dependence on massive training data by exploiting transfer learning of deep networks pretrained on ImageNet data for the diagnosis of dementia. Transfer learning can significantly reduce the length of the training, validation and testing process on a new dataset, and is based on the use of pretrained models which have demonstrated better performance than models trained from scratch in several applications. In this context, the paper investigates the potential of transfer learning, which is based on modifications of the AlexNet and VGG16 convolutional neural networks (CNNs), when MRI or DTI data are used for the classification of Mild Cognitive Impairment (MCI), AD and normal patient. Experiments based on data from the ADNI database demonstrate the high performance of the transfer learning methods in the detection of early degenerative changes in the brain. The highest accuracy of 99.75% in the diagnosis of AD was achieved with transfer learning of VGG models using DTI scans. The prediction of early cognitive decline with an accuracy of 93% was reached by VGG models processing MRI data.
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