Efficient remote sensing image classification using the novel STConvNeXt convolutional network by Bo Liu, Chenmei Zhan, Cheng Guo, Xiaobo Liu, Shufen Ruan

“…Abstract Remote sensing images present formidable classification challenges due to their complex spatial organization, high inter-class similarity, and significant intra-class variability. To address the balance between computational efficiency and feature extraction capability in existing methods, this paper innovatively proposes a lightweight convolutional network, STConvNeXt. …”
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Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement by Shahid Mehmood, Tariq Shahzad, Muhammad Zubair, Farman Matloob Khan, Muhammad Adnan Khan, Khmaies Ouahada, Amir H. Gandomi

“…The conventional method of examining bone marrow cells by hematologists and pathologists using microscopy is tedious, time-consuming, and prone to variability among observers. Hence, there is a demand for a rapid and precise WBCs classification model. …”
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Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation by Jan Kassubek, Francesco Roselli, Simon Witzel, Johannes Dorst, Albert C. Ludolph, Volker Rasche, Ina Vernikouskaya, Hans-Peter Müller

“…Recently, we have introduced automatic hypothalamic quantification method based on the use of convolutional neural network (CNN) to reduce human variability and enhance analysis robustness. …”
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Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification by Bo Yuan, Yudie Hu, Yan Liang, Yutong Zhu, Lingyu Zhang, Shimin Cai, Rui Peng, Xianbin Wang, Zheng Yang, Jinhui Hu

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Week-Ahead Water Demand Forecasting Using Convolutional Neural Network on Multi-Channel Wavelet Scalogram by Adithya Ramachandran, Hatem Mousa, Andreas Maier, Siming Bayer

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3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology by Isaac Y. Tian, Jason Liu, Michael C. Wong, Nisa N. Kelly, Yong E. Liu, Andrea K. Garber, Steven B. Heymsfield, Brian Curless, John A. Shepherd

“…All coefficients of determination (R 2) for all predicted variables were above 0.86 and achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.…”
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BTCP: Binary Temporal Convolutional Network-Based Data Prefetcher for Low Inference Latency and Storage Overhead by Chang Ho Ryu, Tae Hee Han

“…We employed positional delta maps to predict the closest future address, integrating address deltas with temporal weights for labeling. BTCP facilitates variable-degree prefetching by incorporating the ratio of irregular addresses into the positional delta map for enhanced training. …”
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Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis. by Trinh Huu Khanh Dong, Liane S Canas, Joseph Donovan, Daniel Beasley, Nguyen Thuy Thuong-Thuong, Nguyen Hoan Phu, Nguyen Thi Ha, Sebastien Ourselin, Reza Razavi, Guy E Thwaites, Marc Modat

“…The fused model was superior to both, with an average AUC = 77.3% [Formula: see text] 4.0% in the test set. The non-imaging variables were more informative in the HIV-positive group, while the imaging features were more predictive in the HIV-negative group. …”
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Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention by Yuxuan Li, Lisha Nie, Fangrong Zhou, Yun Liu, Haoyu Fu, Nan Chen, Qinling Dai, Leiguang Wang

“…Then, to tackle the challenges of scale variability and model practicality, we propose a Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into YOLO11 to create YOLO11s-MSCA. …”
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Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network by Zakarya Hasan Ahmed Abu Alregal, Gehad Abdullah Amran, Ali A. Al-Bakhrani, Saleh Abdul Amir Mohammad, Amerah Alabrah, Lubna Alkhalil, Abdalla Ibrahim, Maryam Ghaffar, Maryam Ghaffar

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Effects of scale on segmentation of Nissl–stained rat brain tissue images via convolutional neural networks by Alexandro Arnal, Olac Fuentes

“…A leading approach uses convolutional neural networks which model anatomical variability and determine cytoarchitectonic boundaries. …”
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Motor Imagery Classification for Brain Computer Interface Using Deep Convolutional Neural Networks and Mixup Augmentation by Haider Alwasiti, Mohd Zuki Yusoff

“…In particular, this study is trying to avoid the need for long EEG data collection sessions, and without combining multiple subjects training datasets, which has a detrimental effect on the classification performance due to the inter-individual variability among subjects. <italic>Methods:</italic> A customized Convolutional Neural Network with mixup augmentation was trained with <inline-formula><tex-math notation="LaTeX">$\scriptstyle \mathtt {\sim }$</tex-math></inline-formula>120 EEG trials for only one subject per model. …”
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Wi-Fi-Enabled Vision via Spatially-Variant Pose Estimation Based on Convolutional Transformer Network by Hyeon-Ju Lee, Seok-Jun Buu

“…To address these challenges, we propose a Convolutional Transformer Network. This architecture integrates convolutional layers for localized spatial feature extraction and transformer layers for global temporal dependency modeling. …”
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Bone Segmentation in Low-Field Knee MRI Using a Three-Dimensional Convolutional Neural Network by Ciro Listone, Diego Romano, Marco Lapegna

“…However, it remains challenging due to anatomical variability and complex bone morphology. Manual segmentation is time-consuming and operator-dependent, fostering interest in automated methods. …”
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Automated program using convolutional neural networks for objective and reproducible selection of corneal confocal microscopy images by Qincheng Qiao, Wen Xue, Jinzhe Li, Wenwen Zheng, Yongkai Yuan, Chen Li, Fuqiang Liu, Xinguo Hou

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Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification by Sofia El Amoury, Youssef Smili, Youssef Fakhri

“…With Magnetic Resonance Imaging (MRI) serving as a cornerstone for diagnosis, manual interpretation by radiologists is time-consuming and prone to inter-observer variability. Recent advances in deep learning, particularly through the application of Convolutional Neural Networks (CNNs), have transformed medical image analysis by enabling automated, high-accuracy feature extraction. …”
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RAMAS-Net: a module-optimized convolutional network model for aortic valve stenosis recognition in echocardiography by Yejia Gan, Wanzhong Huang, Yan Deng, Xiaoying Xie, Yuanyuan Gu, Yaozhuang Zhou, Qian Zhang, Maosheng Zhang, Yangchun Liu

“…Echocardiography is a key diagnostic tool for AS; however, its accuracy is influenced by inter-observer variability, operator experience, and image quality, which can result in misdiagnosis. …”
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Developing an Algorithm for Robotic Precision Application of Crop Protection Products by M. A. Mirzaev

“…(Materials and methods) A robotic device has been developed for variable rate application of plant protection products able to recognize both useful crops and weeds, determine the area of processing, namely the coordinates of the processing center and the processing radius. …”
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Diagnosis Model for Refrigerant Charge Fault under Heating Conditions based on Multi-layer Convolution Neural Network by Cheng Hengda, Chen Huanxin, Li Zhengfei, Cheng Xiangdong

“…The performance of the refrigerant charge fault diagnosis model of variable refrigerant flow (VRF) system was evaluated with graphed experimental data. …”
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Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models by Vlad-Octavian Bolocan, Oana Nicu-Canareica, Alexandru Mitoi, Maria Glencora Costache, Loredana Sabina Cornelia Manolescu, Cosmin Medar, Viorel Jinga

“…Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. …”
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