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

“…This unbiased CNN-based hypothalamus volume quantification study demonstrated similarly reduced hypothalamus volume in PLS and ALS patients, despite the clinical phenotypic differences.…”
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Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks by Chieh Fu Hsiao, Georg Feyrer, Anthony Stein

“…To this end, in this study, hyperspectral imaging (HSI) data are combined with deep learning models to test the classification ability of two soybean fungal diseases: Asian soybean rust (Phakopsora pachyhizi) and soybean stem rust (Sclerotinia scleroriorum). Different CNNs employing 2D, 3D convolution, and hybrid approaches are compared. …”
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PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network by Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu, Boce Chu

“…The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. …”
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Condition Monitoring of Chain Sprocket Drive System Based on IoT Device and Convolutional Neural Network by Sang Kwon Lee, Jiseon Back, Kanghyun An, Sunwon Kim, Changho Lee, Pungil Kim

“…Multiple-classification performance of the trained network was tested using 100 image samples. Feature maps for different fault types were obtained from the final CNN convolution layer. …”
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VR-Aided Ankle Rehabilitation Decision-Making Based on Convolutional Gated Recurrent Neural Network by Hu Zhang, Yujia Liao, Chang Zhu, Wei Meng, Quan Liu, Sheng Q. Xie

“…This allows for the simulation of five stages of rehabilitation based on the Brunnstrom staging scale, providing tailored control parameters for virtual training scenarios suited to patients at different stages of recovery. Experiments comparing the classification performance of convolutional neural networks and long short-term memory networks were conducted. …”
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The GAN Spatiotemporal Fusion Model Based on Multiscale Convolution and Attention Mechanism for Remote Sensing Images by Youping Xie, Jun Hu, Kang He, Li Cao, Kaijun Yang, Luo Chen

“…Employing an encoder–decoder architecture, the generator effectively extracts multilevel features, accommodating significant resolution differences between high-resolution and low-resolution images. …”
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Segmentation of pores in carbon fiber reinforced polymers using the U-Net convolutional neural network by Miroslav Yosifov, Patrick Weinberger, Bernhard Plank, Bernhard Fröhler, Markus Hoeglinger, Johann Kastner, Christoph Heinzl

“…The model performs well on datasets with both high and low resolution, and even works reasonably for barely visible pores with different shapes and size. In our experiments, we could show that U-Net is suitable for pore segmentation. …”
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OCSCNet-Tracker: Hyperspectral Video Tracker Based on Octave Convolution and Spatial–Spectral Capsule Network by Dong Zhao, Mengyuan Wang, Kunpeng Huang, Weixiang Zhong, Pattathal V. Arun, Yunpeng Li, Yuta Asano, Li Wu, Huixin Zhou

“…The approach enhances separability and establishes relationships between different components and targets at various scales. …”
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Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network by Ning Yuan, Donghai Guan, Shengrong Li, Li Zhang, Qi Zhu

“…This confidence score serves as a weight to assess the relative importance of different time windows. Finally, the confidence-weighted fused features are passed through a multilayer perceptron (MLP) for final classification. …”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

“…In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. …”
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Exploring Applications of Convolutional Neural Networks in Analyzing Multispectral Satellite Imagery: A Systematic Review by Antonia Ivanda, Ljiljana Šerić, Maja Braović

“…This review addresses three Research Questions (RQ): RQ1: “In which application domains different CNN models have been successfully applied for processing MSI data?”…”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…We constructed the ORaph8K dataset, containing 8,000 images of Oudemansiella raphanipes at different growth stages, used for training and validation. …”
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IFNet: An Interactive Frequency Convolutional Neural Network for Enhancing Motor Imagery Decoding From EEG by Jiaheng Wang, Lin Yao, Yueming Wang

“…Methods: Inspired by the concept of cross-frequency coupling and its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency Convolutional Neural Network (IFNet) to explore cross-frequency interactions for enhancing representation of MI characteristics. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

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Multi-task advanced convolutional neural network for robust lymphoblastic leukemia diagnosis, classification, and segmentation by Sercan Yalcin, Zuhal Cetin Yalcin, Muhammed Yildirim, Bilal Alatas

“…The cascaded structure of the MTA-CNN allows the model to learn features at different levels of abstraction, from low-level to high-level, enabling it to capture both fine-grained and coarse-grained information. …”
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Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks by V. Forcadell, V. Forcadell, C. Augros, O. Caumont, O. Caumont, K. Dedieu, M. Ouradou, C. David, J. Figueras i Ventura, O. Laurantin, H. Al-Sakka

“…This study utilises convolutional neural network (CNN) models trained on dual-polarisation radar data to detect severe-hail occurrence on the ground. …”
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Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice by Yuke Ren, Yuke Ren, Shuangxing Li, Di Zhang, Yongtian Zhao, Yanwei Yang, Guitao Huo, Xiaobing Zhou, Xingchao Geng, Zhi Lin, Zhe Qu

“…The images were then randomly divided into training, validation, and test sets in an 8:1:1 ratio. Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. …”
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DBANet: a dual-branch convolutional neural network with attention enhancement for motor imagery classification by Dandan Liang, Brendan Z. Allison, Ruiyu Zhao, Andrzej Cichocki, Jing Jin

“…Firstly, we use a filter bank alignment module, it aligns the multi-frequency data and reduce the differences in the MI data. Subsequently, a spatial-temporal module to extract the spatial-temporal features is employed. …”
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Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection by Filip Postepski, Grzegorz M. Wojcik, Krzysztof Wrobel, Andrzej Kawiak, Katarzyna Zemla, Grzegorz Sedek

“…The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. …”
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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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