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IEEE1d
Deep Learning-based Recognition of Welding Defects in Spiral Submerged Arc Welding Images - IEEE Xplore
This study explores the use of the YOLOv5-master model for defect detection in images of spiral submerged arc weld seams. Given the pivotal role of image preprocessing in optimizing deep learning ...
IEEE1d
Combining Deep Learning with Traditional Features for Classification and Segmentation of Pathological Images of Breast Cancer - IEEE Xplore
The automatic classification of pathological images of breast cancer has important clinical value. In order to improve the accuracy and efficiency of cancer detection, we implement two classifications ...
IEEE2d
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning - IEEE Xplore
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning ...
IEEE2d
Recognition of Medical Images of Eye Diseases in a Deep Learning Perspective - IEEE Xplore
By studying and learning various deep learning models, the most suitable model for identifying diseased eyes, normal eyes, and highly myopic eyes in medical images is selected, and effective ...
Medical Device Network on MSN2d
FDA grants 510(k) clearance for Philips’ deep learning reconstruction software
The US Food and Drug Administration (FDA) has granted 510 (k) clearance for Philips’ SmartSpeed Precise deep learning ...
IEEE2d
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The ...
IEEE5d
Deep Learning-Based Classification of Areca Nut Yellow Leaf Disease with ResNet-50 CNN - IEEE Xplore
Detecting healthy arecanut leaves, yellow leaf disease in arecanut, and differentiating these from other types of leaves using deep learning involves designing an advanced neural network model for ...
IEEE8d
Deepfake Detection through Deep Learning - IEEE Xplore
Deepfakes allow for the automatic generation and creation of (fake) video content, e.g. through generative adversarial networks. Deepfake technology is a controversial technology with many wide ...
IEEE11d
Human Activity Recognition via Wi-Fi CSI and VAE-CNN-BiLSTM Hybrid Deep Learning - IEEE Xplore
Human Activity Recognition (HAR) is vital across multiple applications, such as healthcare monitoring, smart home systems, and surveillance. Recently, Wi-Fi channel state information (CSI) has gained ...
IEEE12d
MSRD-CNN: Multi-Scale Residual Deep CNN for General-Purpose Image Manipulation Detection - IEEE Xplore
The authenticity of digital images is a major concern in multimedia forensics due to the availability of advanced photo editing tools/devices. In the literature, several image forensic methods are ...
IEEE13d
An Application of a Deep Learning Algorithm for Automatic Detection of Unexpected Accidents Under Bad CCTV Monitoring Conditions in Tunnels - IEEE Xplore
In this paper, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and ...
IEEE15d
Deep Learning-Driven Early Detection of Alzheimer’s Disease using CNN - IEEE Xplore
Researchers evaluate Convolutional Neural Networks as tools for early identification and staging of Alzheimer's disease (AD) using MRI data collected from the Open Access Series of Imaging Studies ...