Results 61 to 70 of about 8,226 (198)

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

starU‐Net: An enhanced U‐Net architecture with star operation and multi‐view fusion for improved vessel segmentation

VIEW, EarlyView.
Low‐contrast capillaries are often missed or fragmented by segmentation models, so we developed a shallow four‐level starU‐Net that combines star operation‐based feature extraction with dynamic snake convolution‐based multi‐view fusion for improved thin vessel continuity.
Mengwei Bai, Li Tong, Jing Lin, Peng Huang   +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

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

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

Intelligent Real‐Time Medical Image Transmission for Healthcare‐IoT via Dual‐Stage Probabilistic Restoration and Lightweight Nonlinear Encryption

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Telemedicine and healthcare‐IoT require medical image transmission that preserves diagnostic quality and confidentiality under noise, packet loss and cyber threats. Existing methods often separate restoration from security and lack real‐time efficiency.
Mohamed Amine Midoun, Meryem Hamidaoui, Mohamed Zakariya Talhaoui, Abdelkarim Smaili, Djamel Eddine Mekkaoui, Ling‐Ping Cen   +5 more
wiley   +1 more source

Residual swin transformer for classifying the types of cotton pests in complex background

Frontiers in Plant Science
BackgroundCotton pests have a major impact on cotton quality and yield during cotton production and cultivation. With the rapid development of agricultural intelligence, the accurate classification of cotton pests is a key factor in realizing the precise
Ting Zhang, Jikui Zhu, Fengkui Zhang, Shijie Zhao, Wei Liu, Ruohong He, Hongqiang Dong, Qingqing Hong, Changwei Tan, Ping Li   +9 more
doaj   +1 more source