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Introduction: Alzheimer's disease (AD) is a chronic form of the neurodegenerative disease marked by atrophy in different brain regions. A region-wise analysis is essential for performing AD detection, as each brain region has different functionalities depending on its location. This work aims to investigate supervoxel based volumetric features in place of traditional voxel-based features from the vital brain regions. Methods: In this work, the whole brain structural magnetic resonance imaging (MRI) is segmented into 116 regions using atlas-based segmentation. Important atrophic regions are used for further analysis based on a region ranking procedure from these segmented regions. The focus of this study is to perform supervoxel based partitioning for attaining features prominent for AD detection. Volumetric features are extracted from supervoxels belonging to the selected regions. An optimal feature set is obtained by using the support vector machine recursive elimination (SVM-RFE) method, and classification is performed using SVM. Results: ADNI dataset is used for experimentation. Results are obtained by iteratively fusing the features extracted from vital brain regions. The highest classification accuracy of 90.11%, the sensitivity of 86.11%, and the specificity of 93.4% are obtained by fusing features extracted from hippocampus and amygdala regions. Discussion: The highest classification accuracy reported in this work for AD detection is obtained by fusing features of the four most important regions, i.e., hippocampus and amygdala, in both left and right hemispheres. These regions are also known to affect the consolidation of memory and decision-making in medical science. Experimental results evaluated on the standard dataset depict that the proposed method performs better than the traditional as well as state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Schizophrenia (SCZ) is a severe mental and debilitating neuropsychiatric disorder that disrupts a person's thought processes, emotions, and behavior. Due to misdiagnosis, self-denial, and social stigma, many SCZ cases go untreated. Magnetic resonance imaging (MRI) is an excellent noninvasive tool for soft tissue contrast imaging because it provides crucial data on tissue structure size, position, and shape. The Resnet50 network is a deep residual learning framework used for feature extraction. Random-vector functional link network (RVFL) is an example of a single-hidden-layer feedforward network in which input features and hidden layer features are fed to the output layer. In this paper, we introduced a kernel ridge regression-based random vector functional link (KRR-RVFL) classifier which focuses on addressing the linearity issues in RVFL by designating the kernel function in the input layer for the precise diagnosis of SCZ. The genetic algorithm (GA) seeks to minimize the loss function by optimizing the weights and biases of the KRR-RVFL network. The classification performance is investigated on the SCZ and cognitive normal (CN) subjects, collected from the available open neuro platform, including 99 participants. The results of the suggested network show superior performance to the recent state-of-the-art networks in terms of accuracy 93.66%, sensitivity 92.22%, specificity 95.17%, precision 95.33%, F-measure 93.74%, and G-mean 93.68%. The performance metrics demonstrated the applicability of this framework for assisting clinicians in the automatic, precise evaluation of SCZ. • This paper proposes Schizophrenia diagnosis model using KRR-Opt RVFL classifier. • ResNet50 is deployed for the extraction of complex features from pre-processed data. • GA is incorporated with KRR-RVFL to optimize the parameters of the classifier. • The performance of the proposed KRR-Opt RVFL is compared with the state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a prevalent, irreversible, chronic, and degenerative disorder whose diagnosis at the prodromal stage is critical. Mostly, single modality data, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), are used to make predictions in AD studies. However, the metabolic and structural data fusion can provide a holistic view of AD-staging analysis. To achieve this objective, a novel multimodal fusion-based method is proposed in this article. An optimal fusion of MRI and PET is achieved by harnessing demon algorithm and discrete wavelet transform. Finally, the fused image features are extracted using ResNet-50, and these features are classified using robust energy least square twin support vector machine classifier. Experiments on the AD neuroimaging initiative dataset show descent accuracy of 97%, 94%, and 97.5% for cognitive normal (CN) versus AD, CN versus mild cognitive impairment (MCI), and AD versus MCI, respectively. The proposed model will be beneficial for health professionals in accurately diagnosing AD at an early stage. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer's disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method's source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD. • MRI and PET images are fused to utilize structural and metabolic features for AD diagnosis. • An adversarial learning-based method is proposed to extract fused features. • A coupled adversarial subnetwork and a classification subnetwork have been designed. • Experiments are done on the publicly available ADNI dataset. [ABSTRACT FROM AUTHOR]

Availability of high-quality large annotated data is indeed a significant challenge in healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to large and diverse medical image datasets. To reduce the requirement for annotated training data, self-supervised pre-training strategies on nonannotated data have been extensively used, whereas collaborative algorithm training without the need to exchange the underlying data. In this paper, we introduce a novel federated learning-based self-supervised spatial–temporal transformer's fusion (SSFL) for cardiovascular image segmentation. The integration of spatial–temporal swin transformer is used to extract the features from 3D SAX multiple phases (full cycle of cardiac heart). An efficient self-supervised contrastive framework consisting of a spatial–temporal transformer network with 25 encoders is used to model the temporal features. The spatial and temporal features are fused and forwarded to the decoder for cardiac heart segmentation using cine MRI images. To further improve segmentation, we use an attention-based unpaired GAN model to map or transfer the style from ACDC to M&Ms and use synthetically generated volumes in the proposed self-supervised approach. Experiments with three different cardiovascular image segmentation tasks, such as segmentation of the right ventricle, left ventricle, and myocardium, showed significant improvement compared to the state-of-the-art segmentation framework. • Efficient self-supervised contrastive framework consisting of a spatial–temporal transformer's fusion. • Extracted features from multiple 3D SAX phases (full cardiac heart cycle) for 4D medical image segmentation. • We used 25 encoders to model the temporal features, and passed them to the single decoder. • Uses an attention-based unpaired GAN model to map or transfer the style from ACDC to M&Ms. • Deployed proposed segmentation model in a federated environment. [ABSTRACT FROM AUTHOR]