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Electroencephalogram (EEG) have been widely used for the diagnosis of neurological diseases like epilepsy and sleep disorders. Support vector machines (SVMs) are widely used classifiers for the classification of EEG signals due to their better generalization performance. However, SVM suffers due to high computational complexity. To reduce the computations, twin support vector machines (TWSVM) solved smaller size quadratic optimization problems. To enhance the performance of the SVM and TWSVM models, prior information known as universum data has been incorporated in the universum SVM (USVM) and universum twin (UTSVM) models. Both SVM and UTSVM employ hinge loss which results in sensitivity to noise and instability. To overcome these issues and incorporate the prior information of the EEG signals, we propose a novel universum twin support vector machine with pinball loss function (Pin-UTSVM) for the classification of EEG signals. The proposed Pin-UTSVM is more stable for resampling and is noise insensitive. Furthermore, the computational complexity of proposed Pin-UTSVM model is similar to the standard UTSVM model. In the proposed approach, we used the interictal EEG signal as the universum data. Numerical experiments at varying level of noise show that the proposed Pin-UTSVM is more robust to noise compared to standard models. To show the efficiency of the proposed Pin-UTSVM model, we used multiple feature extraction techniques for the classification of the EEG signal. Experimental results reveal that the proposed Pin-UTSVM model is performing better compared to the existing models. Moreover, statistical tests show that the proposed Pin-UTSVM model is significantly better in comparison with the existing baseline models. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on advanced machine learning algorithms for biomedical data and image processing. Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance, and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation and to explore large-scale data in biomedical sciences. [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]

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]

Online healthcare applications have grown more popular over the years. For instance, telehealth is an online healthcare application that allows patients and doctors to schedule consultations, prescribe medication, share medical documents, and monitor health conditions conveniently. Apart from this, telehealth can also be used to store a patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However, face recognition systems are not foolproof. They are prone to malicious attacks like printed photos, paper cutouts, replayed videos, and 3D masks. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance, existing methods use a significant amount of parameters, making them resource-heavy and unsuitable for handheld devices. Apart from this, they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries, classification becomes more accurate. We further demonstrate our model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods. [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]

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]

• The proposed two-layer Convolutional Neural Network (CNN) is able to learn the high-level features. • The feature maps extracted by the proposed CNN-based model are sparse and selective. • Adopted an averaging model approach for is training several different models on subsets of dataset. • The proposed model generates a pool of features for training and selecting effective classifiers. • An improvement in the discriminative power of face recognition system with a small sample of datasets. This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets. [ABSTRACT FROM AUTHOR]

• Inspired by the principles of Random Vector Functional Link (RVFL) network, we propose a deep RVFL network (dRVFL) with rich feature extraction capabilities through several hidden layers. • We also propose an ensemble deep network (edRVFL) based on a single dRVFL network. • We demonstrate the generic nature of the proposed methods by integrating them with a recent RVFL variant called sparse-pretrained RVFL (SP-RVFL). • Experiments on 46 tabular UCI classification datasets demonstrate that the proposed ensemble deep RVFL networks outperform state-of-the-art deep feed-forward neural networks. • Experiments on 12 sparse classification datasets demonstrate that the proposed ensemble deep SP-RVFL networks outperform the best. In this paper, we propose deep learning frameworks based on the randomized neural network. Inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with stacked layers. The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed-form solution as in a standard RVFL network. We also propose an ensemble deep network (edRVFL) that can be regarded as a marriage of ensemble learning with deep learning. Unlike traditional ensembling approaches that require training several models independently from scratch, edRVFL is obtained by training a single dRVFL network once. Both dRVFL and edRVFL frameworks are generic and can be used with any RVFL variant. To illustrate this, we integrate the deep learning RVFL networks with a recently proposed sparse pre-trained RVFL (SP-RVFL). Experiments on 46 tabular UCI classification datasets and 12 sparse datasets demonstrate that the proposed deep RVFL networks outperform state-of-the-art deep feed-forward neural networks (FNNs). [ABSTRACT FROM AUTHOR]