Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

“…By leveraging advanced deep learning techniques, including 3D-Echo Fusion, our approach promises high classification accuracy while reducing reliance on manual annotations, thus enhancing scalability in cardiac diagnostics.…”
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A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification by Xin Deng, Huaxiang Huo, Lijiao Ai, Daijiang Xu, Chenhui Li

“…This approach transforms EEG signals into a three-dimensional data structure, utilizing one-dimensional convolutions along the temporal dimension and two-dimensional convolutions across the EEG electrode distribution for initial spatio-temporal feature extraction, followed by deep feature exploration using a 3D Swin Transformer module. …”
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P213: Interdisciplinary variant re-classification: FGFR3 as an example of genotypic investigation in suspected skeletal dysplasia population by Ashley Volz, Mitch Bailey, Keith Nykamp, Naomi Ziv, Chris Bauer

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Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

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Research on CTSA-DeepLabV3+ Urban Green Space Classification Model Based on GF-2 Images by Ruotong Li, Jian Zhao, Yanguo Fan

Subjects: “…urban green space classification…”
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A Classification Method for Cerebral Aneurysms in TOF-MRA Based on Improved 3D ResNet50 Model by XUE Peiyang, GENG Chen, LI Yuxin, BAO Yifang, LU Yucheng, DAI Yakang

“…In this paper, we propose an improved parallel multiscale fusion attention network (PMAF-Net) based on 3D ResNet50 for classification. PMAF-Net uses multi-scale convolution and weighted fusion channel and spatial attention weights to enhance the feature extraction capability. …”
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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

Subjects: “…Dynamic 3-D convolution…”
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Enhanced Bird Species Image Recognition and Classification using MobileNet and InceptionV3 Transfer learning Architectures by Sakthi Priya G, Vignesh Saravanan K, Dheetchana K

“… The proposed study explores the application of transfer learning techniques in bird species image classification, specifically focusing on the MobileNet and InceptionV3 models. …”
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Efficient 3D convolutional neural networks for Sentinel-2 land cover classification with limited ground truth data by Gabriel A. Carneiro, Jan Svoboda, António Cunha, Joaquim J. Sousa, Přemysl Štych

“…In this study, we evaluated the performance of window-pixel-wise 2D, 3D, and 3D Multiscale CNN architectures for land cover classification. 3D and 3D multiscale CNNs were using the spectral dimension as the third dimension for convolutions. …”
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RE-BPFT: An Improved PBFT Consensus Algorithm for Consortium Blockchain Based on Node Credibility and ID3-Based Classification by Junwen Ding, Xu Wu, Jie Tian, Yuanpeng Li

“…Additionally, we adopt an optimized ID3 decision-tree method for node classification, dynamically identifying high-performing, trustworthy, ordinary, and malicious nodes based on real-time data. …”
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Novel 3-D Deep Neural Network Architecture for Crop Classification Using Remote Sensing-Based Hyperspectral Images by Mahmood Ashraf, Lihui Chen, Nisreen Innab, Muhammad Umer, Jamel Baili, Tai-Hoon Kim, Imran Ashraf

“…Furthermore, a shortage of labeled data makes achieving high classification accuracy challenging. This study proposes an improved 3-D UNet architecture based on a modified convolutional neural network that uses spatial and spectral information. …”
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ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging by Carlos Osorio Quero, Daniel Durini, Jose Martinez-Carranza

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Integration of YOLOv8 Small and MobileNet V3 Large for Efficient Bird Detection and Classification on Mobile Devices by Axel Frederick Félix-Jiménez, Vania Stephany Sánchez-Lee, Héctor Alejandro Acuña-Cid, Isaul Ibarra-Belmonte, Efraín Arredondo-Morales, Eduardo Ahumada-Tello

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Recursive Class Connectivity Classification (R3C) Applied to Binary Image Segmentation for Improved Infant Fingerprint Enhancement by Joao Leonardo Harres Dall Agnol, Luiz Fernando Puttow Southier, Jefferson Tales Oliva, Marcelo Teixeira, Rodrigo Minetto, Marcelo Filipak, Dalcimar Casanova, Erick Oliveira Rodrigues

“…This study introduces Recursive Class Connectivity Classification (R3C), a novel framework that iteratively refines binary segmentation outputs from existing enhancement methods by extending ridge structures. …”
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3D Micro-Expression Recognition Based on Adaptive Dynamic Vision by Weiyi Kong, Zhisheng You, Xuebin Lv

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Classification of inferior articular process injury after percutaneous endoscopic interlaminar lumbar discectomy based on CT three-dimensional reconstruction and its clinical significance by Haigang Hu, Chao Wu, Tao Li, Hong Li

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The Relation between Morphology of Maxillary Sinus after Augmentation in Three Classification Methods and Residual Bone Height: A Retrospective Study by Zhi Wang, Qi Jia, Heng Bo Jiang, Jianmin Han, Lidong Zou, Guangliang Niu

“…There was a significant correlation between the three different evaluation classifications (p<0.05). …”
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Hepatotoxicity Evaluation of Levornidazole and Its Three Main Impurities: Based on Structure–Toxicity Classification Prediction Combined with Zebrafish Toxicity Assessment by Ting Liu, Song Yuan, Luyong Zhang, Dousheng Zhang

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Protocol for the three-dimensional analysis of rodent skeletal muscle by Smrithi Karthikeyan, Yoko Asakura, Mayank Verma, Atsushi Asakura

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AGE AND GENDER CLASSIFICATION FROM IRIS IMAGES OF THE EYE USING MACHINE LEARNING TECHNIQUES by Martins E. Irhebhude, Adeola O. Kolawole, Halima Abemi

“…Next, the Support Vector Machine (SVM) was applied to classify the iris images into the six groups recording various performance values. The 3D histogram with PCA recorded an excellent classification performance accuracy of 99.27% as against the EfficientNet deep learning model which recorded 52.29%. …”
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