Results 81 to 90 of about 28,970 (210)

JPEG Steganalysis Based on DenseNet

, 2017
Different from the conventional deep learning work based on an images content in computer vision, deep steganalysis is an art to detect the secret information embedded in an image via deep learning, pose challenge of detection weak information invisible hidden in a host image thus learning in a very low signal-to-noise (SNR) case.
Yang, Jianhua, Shi, Yun-Qing, Wong, Edward K., Kang, Xiangui   +3 more
openaire   +2 more sources

Automated Melanocytic Lesion Classification: Capsule Networks Trained With Synthetic Images Can Outperform Networks Trained With Real Images

Australasian Journal of Dermatology, EarlyView.
ABSTRACT Background/Objectives Convolutional neural networks (CNNs) are known, due to inherent flaws in their design, to be subject to classification error. Many of these shortcomings in classification performance were addressed in 2017 with the introduction of capsule networks (CNs).
Hayley Chai, Stephen Gilmore
wiley   +1 more source

Boosting Rice Disease Diagnosis: A Systematic Benchmark of Five Deep Convolutional Neural Network Models in Precision Agriculture

Agriculture
Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking.
Shu-Hung Lee, Qi-Wei Jiang, Chia-Hsin Cheng, Yu-Shun Tsai, Yung-Fa Huang   +4 more
doaj   +1 more source

Multi‐angle, cross‐domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae)

Cladistics, EarlyView.
Abstract Automated insect identification systems hold significant value for biodiversity monitoring, pest management, citizen science initiatives and systematic studies, particularly in an era of declining expertise in insect taxonomy. However, current deep learning approaches often rely on standardized specimen photos from limited‐angles and ...
Xinkai Wang, Huaiyu Liu, Zhuo Chen, Yisheng Zhao, Yingqi Liu, Haoyang Xiong, Yuange Duan, Fang Song, Wanzhi Cai, Xuankun Li, Hu Li   +10 more
wiley   +1 more source

DenseNet for Anatomical Brain Segmentation

Procedia Computer Science, 2018
Abstract Automated segmentation in brain magnetic resonance image (MRI) plays an important role in the analysis of many diseases and conditions. In this paper, we present a new architecture to perform MR image brain segmentation (MRI) into a number of classes based on type of tissue. Recent work has shown that convolutional neural networks (DenseNet)
Ram Deepak Gottapu, Cihan H Dagli
openaire   +1 more source

Textile and colour defect detection using deep learning methods

Coloration Technology, EarlyView.
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui, Abdel‐Fattah Seyam, Renzo Shamey   +2 more
wiley   +1 more source

Artificial Intelligence in the Detection of Clinically Negotiable Second Mesio‐Buccal Canals in Periapical Images of Maxillary Molars

International Endodontic Journal, EarlyView.
ABSTRACT Aim Artificial intelligence (AI) has the potential to aid clinicians in assessing case difficulty in endodontics. The objectives of this study were to develop and validate deep learning models for the detection of clinically negotiable MB2 canals in periapical images of maxillary first and second molars, and to compare the performance of AI ...
Seyed AmirHossein Ourang, Fatemeh Sohrabniya, Soroush Sadr, Jane Lee, Noreen Ramzy, Alan S. Law, Ernest W. N. Lam, Asma Ahmed Khan, Ali Nosrat   +8 more
wiley   +1 more source

Sector Classification of Unerupted Maxillary Canines: A Deep Learning‐Based Automated Framework Using Panoramic Radiographs

Orthodontics &Craniofacial Research, EarlyView.
ABSTRACT Objectives To develop a deep learning‐based framework to automate sector classification of unerupted maxillary canines (UMCs), assessing its accuracy and reliability compared to human ones. Material and Methods One thousand five hundred twenty‐eight UMCs from digital panoramic radiographs (PRs) were selected using data from the Dental ...
Marzio Galdi, Davide Cannatà, Flavia Celentano, Luigia Rizzo, Domenico Rossi, Tecla Bocchino, Genoveffa Tortora, Stefano Martina   +7 more
wiley   +1 more source

A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features

Orthopaedic Surgery, Volume 17, Issue 5, Page 1513-1524, May 2025.
This study developed an automated distal radius fracture classification system based on statistical shape model (SSM) feature extraction and neural network classification. The method first extracts point cloud data from CT images, then extracts fracture features through registration, downsampling, and PCA dimensionality reduction, before inputting them
Xing‐bo Cai, Ze‐hui Lu, Zhi Peng, Yong‐qing Xu, Jun‐shen Huang, Hao‐tian Luo, Yu Zhao, Zhong‐qi Lou, Zi‐qi Shen, Zhang‐cong Chen, Xiong‐gang Yang, Ying Wu, Sheng Lu   +12 more
wiley   +1 more source

Advances in cardiac devices and bioelectronics augmented with artificial intelligence

The Journal of Physiology, EarlyView.
Abstract figure legend Interfaces between the human heart, diagnostic bioelectronics, artificial intelligence, and clinical care. From left to right: Human heart and biosensor interface; representative waveforms of common diagnostic bioelectronic sensing modalities.
Charles Stark, Eric Rytkin, Andrei Alexandru Mircea, Igor R. Efimov   +3 more
wiley   +1 more source