Multiscale Attention Feature Fusion Based on Improved Transformer for Hyperspectral Image and LiDAR Data Classification by Aili Wang, Guilong Lei, Shiyu Dai, Haibin Wu, Yuji Iwahori

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Enhancing Point Cloud Classification and Segmentation With Attention-Enhanced SO-PointNet++ by Gang Cheng, Chengwei Gu

“…With the rapid advancement of deep learning in computer vision, accurately performing classification and segmentation tasks on 3D point clouds has become increasingly important. …”
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Клинико-топографическое описание соотношения апексов зубов и дна гайморовой пазухи как анатомический аспект классификации одонтогенных гайморитов...

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3D tooth identification for forensic dentistry using deep learning by Hamza Mouncif, Amine Kassimi, Thierry Bertin Gardelle, Hamid Tairi, Jamal Riffi

Subjects: “…3D mesh processing…”
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Variability of three‐dimensional knee morphology cannot be effectively assessed using a coronal plane knee alignment classification in total knee arthroplasty patients by Ishaan Jagota, Joshua Twiggs, Brad Miles, Jonathan V. Baré

Subjects: “…CPAK classification system…”
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Pain Level Classification Using Eye-Tracking Metrics and Machine Learning Models by Oussama El Othmani, Sami Naouali

“…XGBoost achieves the highest classification accuracy of 99.5%, demonstrating its robustness for pain level classification on a scale from 0 to 5. …”
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An Innovative Stepwise C‐Means Clustering Approach for Classification of Adolescent Idiopathic Scoliosis by Jiale Gong, Zifang Zhang, Yunzhang Cheng, Liang Cheng, Yating Dong, Lin Sha, Qin Fan, Jian Chen, Chaomeng Wu, Wenyuan Sui, Yaqing Zhang, Fuyun Liu, Weiming Hu, Wenqing Wei, Junlin Yang

Subjects: “…3D classification…”
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Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks by Huong Hoang Luong, Hai Thanh Nguyen, Nguyen Thai-Nghe

“…As a result, the classification in our model based on the custom EfficientNetB3 model and seam carving technique received a great validation accuracy, test accuracy, and F1 score throughout three scenarios at 96.73%, 97.59%, and 97.58%, respectively. …”
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Data Driven Classification of Opioid Patients Using Machine Learning–An Investigation by Lisan Al Amin, Md. Saddam Hossain Mukta, Md. Sezan Mahmud Saikat, Md. Ismail Hossain, Md. Adnanul Islam, Mohiuddin Ahmed, Sami Azam

“…Dependable and reliable classification of opioid dependent patients from well-grounded data sources is essential. …”
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Rotation-Invariant Convolution With Point Sort and Curvature Radius for Point Cloud Classification and Segmentation by Zhao Shen, Xin Jia, Jinglei Zhang

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A Dense Bootstrap Contrastive Learning Method With 3-D Dynamic Convolution for Few-Shot PolSAR Image Classification by Nana Jiang, Wenbo Zhao, Jiao Guo, Xiuya Dong, Jubo Zhu

Subjects: “…3-D dynamic convolution (3DDC)…”
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OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification by Irshad Khalil, Asif Mehmood, Hyunchul Kim, Jungsuk Kim

“…Through experimentation and simulation, the proposed OCTNet improves the classification accuracy of the InceptionV3 model by 1.3%, yielding higher accuracy than other SOTA models. …”
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A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification by Hongwei Wei, Yufan Wang, Yu Sun, Jianfeng Zheng, Xiaodong Yu

“…Our tests, which included eight distinct approaches for classification and three well-known HSI data sets, revealed that our recently built model, J-NHPR, can provide comparative advantage (in terms of classification accuracy) over other HSI classification methods. …”
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Application of 3D ELD_MobileNetV2 Incorporating Attention Mechanism and Dilated Convolution in Hepatic Nodules Classification by SUN Haoyun, WANG Lijia

Subjects: “…3d mobilenetv2…”
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Performance Evaluation of 3-D Convolutional Neural Network for Multitemporal Flood Classification Framework With Synthetic Aperture Radar Image Data by Dodi Sudiana, Indra Riyanto, Mia Rizkinia, Rahmat Arief, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo, Ketut Wikantika

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The need for improved identification and accurate classification of stages 3-5 Chronic Kidney Disease in primary care: retrospective cohort study. by Poorva Jain, Melanie Calvert, Paul Cockwell, Richard J McManus

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Enhancing Arrhythmia Diagnosis Through ECG Deep Learning Classification Deploying and Augmented Reality 3D Heart Visualization and Interaction by Kahina Amara, Mohamed Amine Guerroudji, Oussama Kerdjidj, Nadia Zenati, Shadi Atalla, Naeem Ramzan

“…To address this challenge, we have developed ArythmiAR, a novel system that integrates Convolutional Neural Networks (CNN) with Augmented Reality (AR) to enable interactive diagnosis with 3D visualisation and real-time engagement. ArythmiAR offers several key innovations: deep learning-based ECG classification for precise arrhythmia detection, 3D heart modelling and assembly for detailed visualisation, an AR interface for deploying CNN models, 3D localisation of heart sub-regions responsible for arrhythmia anomalies, and enhanced 3D visualisation and interaction capabilities. …”
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Interchangeability of Cross-Platform Orthophotographic and LiDAR Data in DeepLabV3+-Based Land Cover Classification Method by Shijun Pan, Keisuke Yoshida, Satoshi Nishiyama, Takashi Kojima, Yutaro Hashimoto

“…Furthermore, LiDAR data were visualized using high-contrast color scales to improve the accuracy of land cover classification methods through image fusion techniques. …”
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Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images by Mingzhi Zhang, Tsz Kin Ng, Yi Zheng, Guihua Zhang, Jian-Wei Lin, Ji Wang, Jie Ji, Peiwen Xie, Yongqun Xiong, Hanfu Wu, Cui Liu, Huishan Zhu, Jinqu Huang, Leixian Lin

“…Objectives To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.Design A multicentre, platform-based development study using retrospective and cross-sectional data. …”
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Prediction of Clavien Dindo Classification ≥ Grade III Complications After Epithelial Ovarian Cancer Surgery Using Machine Learning Methods by Aysun Alci, Fatih Ikiz, Necim Yalcin, Mustafa Gokkaya, Gulsum Ekin Sari, Isin Ureyen, Tayfun Toptas

“…True positive (TP) rate, False positive (FP) rate, precision, recall, and Area under the curve (AUC) values were evaluated to demonstrate clinical usability and classification skills. <i>Results</i>: 139 patients (77.65%) had no morbidity or grade I-II CDC morbidity, while 40 patients (22.35%) had grade III or higher CDC morbidity. …”
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