Results 71 to 80 of about 2,105 (150)

Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning

Bioengineering
Deep learning has achieved widespread adoption for medical image diagnosis, with extensive research dedicated to mammographic image analysis for breast cancer screening.
Kenji Yoshitsugu, Kazumasa Kishimoto, Tadamasa Takemura   +2 more
doaj   +1 more source

A Multi‐Sequence Adversarial Fusion U‐Net for Brain Tumor Image Segmentation

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
In the field of brain tumor image segmentation, in order to avoid the impact of insufficient number of training samples, the method of fusing multi‐modal MRI information before segmentation is widely used. However, when fusing different modal features, existing methods only add fixed weights to the features of each modality, resulting in insufficient ...
Jie Wang, Jinglu Hu
wiley   +1 more source

Swin-FER: Swin Transformer for Facial Expression Recognition

Applied Sciences
The ability of transformers to capture global context information is highly beneficial for recognizing subtle differences in facial expressions. However, compared to convolutional neural networks, transformers require the computation of dependencies between each element and all other elements, leading to high computational complexity. Additionally, the
Mei Bie, Huan Xu, Yan Gao, Kai Song, Xiangjiu Che   +4 more
openaire   +2 more sources

AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam, Basit Raza, Muhammad Faheem, Aadil Raza   +3 more
wiley   +1 more source

Swin-HViT for accurate crop disease prediction using an adaptive hybrid transformer model

Discover Artificial Intelligence
Agriculture plays a pivotal role in global economic growth, yet it faces significant challenges from pests and crop diseases. Early detection is crucial for preventing large-scale crop losses and ensuring food security.
Hemalatha Gunasekaran, N. R. Wilfred Blessing, Naveen Vijayakumar Watson, B. Hariharan, Angelin Gladys Jesudoss, C. G. Anupama   +5 more
doaj   +1 more source

DrLS: Distortion‐Resistant Lossless Steganography via Colour Depth Interpolation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT The lossless data steganography is to hide a certain amount of information into a container image. Previous lossless steganography methods fail to strike a balance between capacity, imperceptibility, accuracy, and robustness, commonly vulnerable to distortion on container images.
Youmin Xu, Xuanyu Zhang, Xiandong Meng, Jian Zhang   +3 more
wiley   +1 more source

Auto‐ISC: Inter‐Sample Contrastive Learning for Automated Curve Angle Calculation From Ultrasound

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Scoliosis assessment has been increasingly supported by AI‐assisted tools, enabling more efficient and standardised clinical workflows. However, existing ultrasound‐based pipelines often suffer from limited generalisation and unstable performance.
Chen Zhang, Anh Hoang Phuc Nguyen, Yongping Zheng, Sai Ho Ling   +3 more
wiley   +1 more source

Dissecting Glioma Heterogeneity: A Deep Hybrid Graph Convolutional Network With Hinge Attention for Causal‐Effect Explainability

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Brain tumour classification is a critical task in medical imaging that requires accurate and interpretable solutions to assist in clinical decision‐making. In this paper, we present GraphConvNet‐X, a novel hybrid model that integrates convolutional neural networks (CNNs) for spatial feature extraction with graph neural networks (GNNs) that ...
Sultanul Arifeen Hamim, Mubasshar U. I. Tamim, S. M. Hasan Mahmud, M. F. Mridha, Zeyar Aung   +4 more
wiley   +1 more source

Visualizing Image Segmentation Network Behavior Through the Lens of Scale Space Analysis

Computer Graphics Forum, EarlyView.
Abstract Deep neural networks are widely used for image segmentation, also in sensitive applications such as medical imaging or autonomous driving. However, few explainable AI methods are available that help developers understand such networks beyond classification.
A. C. Mikliss, T. Schultz
wiley   +1 more source

Comparing deep learning models for butterfly and moth (Lepidoptera) species identification

Insect Conservation and Diversity, EarlyView.
Deep learning models open new possibilities for the processing of image‐based species records. We used high‐performance computing to compare 40 models for butterfly and moth species identification based on a citizen science dataset with over 500,000 images of 162 species.
Friederike Barkmann, Andreas Lindner, Johannes Rüdisser   +2 more
wiley   +1 more source