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Schizophrenia (SCZ) is a devastating mental condition with significant negative consequences for patients, making correct and prompt diagnosis crucial. The purpose of this study is to use structural magnetic resonance image (MRI) to better classify individuals with SCZ from control normals (CN) and to locate a region of the brain that represents abnormalities associated with SCZ. Deep learning (DL), which is based on the nervous system, could be a very useful tool for doctors to accurately predict, diagnose, and treat SCZ. Gray Matter (GM), Cerebrospinal Fluid (CSF), and White Matter (WM) brain regions are extracted from 99 MRI images obtained from the open-source OpenNeuro database to demonstrate SCZ's regional relationship. In this paper, we use a pretrained ResNet-50 deep network to extract features from MRI images and an ensemble deep random vector functional link (edRVFL) network to classify those features. By examining the results obtained, the edRVFL deep model provides the highest classification accuracy of 96.5% with WM and is identified as the best-performing algorithm compared to the traditional algorithms. Furthermore, we examined the GM, WM, and CSF tissue volumes in CN subjects and SCZ patients using voxel-based morphometry (VBM), and the results show 1363 significant voxels, 6.90 T-value, and 6.21 Z-value in the WM region of SCZ patients. In SCZ patients, WM is most closely linked to structural alterations, as evidenced by VBM analysis and the DL model. [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 the most pervasive form of dementia, resulting in severe psychosocial effects such as affecting personality, reasoning, emotions, and memory. Several neuroimaging techniques are available to correctly identify the structural changes in the brain, out of which the most popular is structural T-1 weighted Magnetic Resonance Imaging (MRI). From 3D MRI, sagittal plane slices provide more clear information related to the hippocampus, amygdala, corpus callosum, and several vital regions of the brain, which defines the extent of degeneration of the AD. Although diverse analysis of machine learning (ML) and deep learning (DL) based algorithm is already proposed for diagnosis of AD, still there is scope of research for early prediction so that treatment can be started either by medication or by improving the lifestyle. This paper proposed a DL model for all level feature extraction and fuzzy hyperplane based least square twin support vector machine (FLS-TWSVM) for the classification of the extracted features for early diagnosis of AD (FDN-ADNet) using extracted sagittal plane slices from 3D MRI images. Model is trained over the online available ADNI dataset and triangular fuzzy function is applied for the construction of hyperplane for classification. The proposed model attains the highest accuracy of 97.15%, 97.29% and 95% for CN vs AD, CN vs MCI and AD vs MCI classification, respectively when compared with the several state of the art networks. • Alzheimer's disease (AD) is the most prevailing irreversible mental neural disorder which leads to memory deterioration. • Sagittal plane of MRI provides more visual features of the mid brain regions. • Pre-processing is done on MRI images for sagittal plane extraction, image registration and key slice extraction. • ResNet101 deep learning network (DLN) is implemented to extract the features from sagittal plane slices. • Fuzzy based classifier is used for classification of the features extracted from DLN for classification of CN vs AD, Cn vs MCI, AD vs MCI. [ABSTRACT FROM AUTHOR]

Deep kernel learning has been well explored for multi-class classification tasks; however, relatively less work is done for one-class classification (OCC). OCC needs samples from only one class to train the model. Most recently, kernel regularized least squares (KRL) method-based deep architecture is developed for the OCC task. This paper introduces a novel extension of this method by embedding minimum variance information within this architecture. This embedding improves the generalization capability of the classifier by reducing the intra-class variance. In contrast to traditional deep learning methods, this method can effectively work with small-size datasets. We conduct a comprehensive set of experiments on 18 benchmark datasets (13 biomedical and 5 other datasets) to demonstrate the performance of the proposed classifier. We compare the results with 16 state-of-the-art one-class classifiers. Further, we also test our method for 2 real-world biomedical datasets viz.; detection of Alzheimer's disease from structural magnetic resonance imaging data and detection of breast cancer from histopathological images. Proposed method exhibits more than 5% F 1 score compared to existing state-of-the-art methods for various biomedical benchmark datasets. This makes it viable for application in biomedical fields where relatively less amount of data is available. [ABSTRACT FROM AUTHOR]

Structural magnetic resonance imaging (sMRI) has been used to examine age-related neuroanatomical changes in the human brain. In the present work, a pre-trained deep learning model and an ensemble deep random vector functional link (edRVFL) classifier have been used to create a brain age classification framework from magnetic resonance imaging (MRI) scans. A total of 155 MRI scans of the brain are obtained from the open-access OpenNeuro database and categorized into three age groups (3–5 years old, 7–12 years old, and 18–40 years old). To visualize the age connection across different brain regions, all MRI scans are first segmented into Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). The ResNet-50 network is used to extract features from MRI images, while the edRVFL network is used to classify the retrieved features. Classification accuracy for GM, WM, CSF, and whole brain images are 96.11%, 98.33%, 93.33%, and 94.00%, respectively, using the edRVFL classifier. Region-wise analysis has also been done using Pearson's correlation coefficient (r), coefficient of determination (R 2), and root mean square error (RMSE) to analyze the relationship between brain age and brain tissue volumes. According to the findings of the suggested deep model for brain age categorization, and region-wise analysis, alterations in WM volume are strongly linked to brain aging. [ABSTRACT FROM AUTHOR]