Abstract Functional Magnetic Resonance Imaging (fMRI) is presently one of the most
popular techniques for analysing the dynamic states in brain images using various kinds of …

Neural network (NN) finds role in variety of applications due to combined effect of feature
extraction and classification availability in deep learning algorithms. In this paper, we have …

Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify
the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to …

Euler's Elastica is a common approach developed based on minimizing the elastica energy.
It is one of the effective approaches to solve the image inpainting problem. Nevertheless …

Deep-learning methods based on deep neural networks (DNNs) have recently been
successfully utilized in the analysis of neuroimaging data. A convolutional neural network …

Electroencephalogram (EEG) channel optimization can reduce redundant information and
improve EEG decoding accuracy by selecting the most informative channels. This article …

This paper introduces logistic regression with sparse and smooth regularizations (LR-SS), a
novel framework that simultaneously enhances both classification and feature extraction …

Reconstructing visual stimuli from brain activity measured by functional magnetic resonance
imaging (fMRI) is challenging for fMRI-based decoding. Some previous studies …

The brain structure–function relationship is crucial to how the human brain works under
normal or diseased conditions. Exploring such a relationship is challenging when using the …

Image augmentation is crucial for generating ample data in fields like medical image
classification, especially when patient samples are limited, demanding high clinical …