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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Improving SOC estimation in low-relief farmlands using time-series crop spectral variables and harmonic component variables based on minimum sample size by Chenjie Lin, Ling Zhang, Nan Zhong

“…The results showed that: (1) time-series NDVI was established as the characteristic crop spectral variables, based on crop spectral variables extracted from eight-day time-series reflectance products. (2) Seventeen harmonic component variables were derived from time-series NDVI via Fourier transformation. (3) Six crop spectral variables and seven harmonic component variables were determined as the optimal SOC estimators. (4) The convolutional neural network model provided higher SOC estimation accuracy (R2 = 0.81, NRMSE = 7.09%) than the random forest model and the back propagation neural network model. …”
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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

“…In this study, we present a novel application of deep 3D convolutional graph networks and nonlinear Gaussian process regression for human body shape parameterization and body composition estimation. …”
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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

“…To address these issues, we propose a binary temporal convolutional network-based data prefetcher (BTCP) that offers advantages in terms of computational efficiency and memory requirements, enabling feasible hardware implementation. …”
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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

“…Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MRI) has not been well investigated. We used a convolutional neural network (CNN) to explore the complementary contribution of brain MRI to the conventional prognostic determinants.…”
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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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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

“…This paper presents a fault diagnosis model based on a convolution neural network. The kernel size and number of neurons of a3-layerconvolutionnetwork were optimized by an orthogonal experiment method. …”
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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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A Reinforced, Event-Driven, and Attention-Based Convolution Spiking Neural Network for Multivariate Time Series Prediction by Ying Li, Xikang Guan, Wenwei Yue, Yongsheng Huang, Bin Zhang, Peibo Duan

“…This paper proposes a reinforced, event-driven, and attention-based convolution SNN model (REAT-CSNN) with three novel features. …”
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Improving Prediction of Marine Low Clouds Using Cloud Droplet Number Concentration in a Convolutional Neural Network by Yang Cao, Yannian Zhu, Minghuai Wang, Daniel Rosenfeld, Chen Zhou, Jihu Liu, Yuan Liang, Kang‐En Huang, Quan Wang, Heming Bai, Yichuan Wang, Hao Wang, Haipeng Zhang

“…CNNMet‐Nd demonstrates superior performance, explaining over 70% of the variance in these three cloud variables for scenes of 1° × 1°, a notable improvement over past efforts. …”
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