Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging,
Electroencephalogram   Dementia   Alzheimer's Disease   Frontotemporal Dementia   Short-Time Fourier Transform   Convolutional Neural Networks  

Background Introduction Dementia is a global health issue that significantly impacts patients’ quality of life and places a substantial burden on healthcare systems. Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) are two common types of dementia, and their overlapping symptoms make accurate diagnosis and targeted treatment development p...

MediVision: Empowering Colorectal Cancer Diagnosis and Tumor Localization through Supervised Learning Classifications and Grad-CAM Visualization of Medical Colonoscopy Images

Information Science, Artificial Intelligence, Medicine, Medical Imaging, Oncology,
Colorectal Cancer   Colonoscopy   Convolutional Neural Networks   Malignant Tumors   Integrated CNN   Grad-CAM  

Academic Background Colorectal Cancer (CRC) is one of the most common cancers worldwide, particularly among individuals over the age of 50. Early detection and accurate diagnosis are crucial for improving patient survival rates. However, traditional CRC screening methods, such as colonoscopy, rely heavily on the experience and visual judgment of ph...

A Temporal Dependency Learning CNN with Attention Mechanism for MI-EEG Decoding

Information Science, Automation, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Temporal Dependency Learning   Convolutional Neural Networks   Brain-Computer Interface   Motor Imagery   Attention Mechanism  

MI-EEG Decoding Using a Temporal Dependency Learning Convolutional Neural Network (CNN) Based on Attention Mechanism Research Background and Problem Description Brain-Computer Interface (BCI) systems provide a new way of communicating with computers by real-time translation of brain signals. In recent years, BCI technology has played an important r...

Modulating Effective Receptive Fields for Convolutional Kernels

Information Science, Artificial Intelligence,
Convolutional Kernels   Convolutional Neural Networks   Effective Receptive Fields   Gaussian Distribution   Image Classification   Object Detection  

GMConv: Adjusting the Effective Receptive Field of Convolutional Neural Networks Introduction Convolutional Neural Networks (CNNs) have achieved significant success in computer vision tasks, including image classification and object detection, through the use of convolutional kernels. However, in recent years, Vision Transformers (ViTs) have gained...