Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms.

Background/objectives: Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge.

High-Resolution Network with Dynamic Convolution and Coordinate Attention for Classification of Chest X-ray Images.

The development of automatic chest X-ray (CXR) disease classification algorithms is significant for diagnosing thoracic diseases. Owing to the characteristics of lesions in CXR images, including high similarity in appearance of the disease, varied sizes, and different occurrence locations, most existing convolutional neural network-based methods have insufficient feature extraction for thoracic lesions and struggle to adapt to changes in lesion size and location. To address these issues, this study proposes a high-resolution classification network with dynamic convolution and coordinate attention (HRCC-Net).

Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies.

Background: Schizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease.

Methods: In this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits.

A crossed-polarized four port MIMO antenna for UWB communication.

This paper presents a compact, crossed-polarized, ultra-wideband (UWB) four-ports multiple-input-multiple-output (MIMO) printed antenna. The proposed antenna is constructed from four microstrip circular patch elements fed by a 50-Ω microstrip line. Two metamaterial cell elements, in the form of a rectangular concentric double split ring resonator (SRR), are placed at the upper plane of the substrates for bandwidth improvement and isolation enhancement.

HMNet: Hierarchical Multi-Scale Brain Tumor Segmentation Network.

An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches.

Altered striatal functional connectivity and structural dysconnectivity in individuals with bipolar disorder: A resting state magnetic resonance imaging study.

Objective: Bipolar disorder (BD) is a mood swing illness characterized by episodes ranging from depressive lows to manic highs. Although the specific origin of BD is unknown, genetics, environment, and changes in brain structure and chemistry may all have a role. Through magnetic resonance imaging (MRI) evaluations, this study looked into functional abnormalities involving the striatum between BD group and healthy controls (HC), compared the whole-brain gray matter (GM) morphological patterns between the groups and see whether functional connectivity has its underlying structural basis.

Gray matter, white matter and cerebrospinal fluid abnormalities in Parkinson's disease: A voxel-based morphometry study.

Background: Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by bradykinesia, tremor, and rigidity among other symptoms. With a 70% cumulative prevalence of dementia in PD, cognitive impairment and neuropsychiatric symptoms are frequent.

Materials And Methods: In this study, we looked at anatomical brain differences between groups of patients and controls.

An Efficient Turbo Decoding and Frequency Domain Turbo Equalization for LTE Based Narrowband Internet of Things (NB-IoT) Systems.

This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement.