3D Convolutional Neural Networks for Brain Tumor Analysis in Multimodal MRI: A Systematic Review by Hamza S. Alsmadi, Hazlina Hamdan, Norwati Mustapha, Noridayu Manshor

“…Convolutional neural networks (CNNs) have emerged as a preferred approach for medical image analysis. …”
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A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

“…We introduce dynamic multiscale convolution attention module, which consists of multiscale context aggregation (MCA) module and dynamic partial convolution attention (DPCATT) module. …”
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An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks by Tuomas Kaprio, Jaana Hagström, Jussi Kasurinen, Ioannis Gkekas, Sofia Edin, Ines Beilmann-Lehtonen, Karin Strigård, Richard Palmqvist, Ulf Gunnarson, Camilla Böckelman, Caj Haglund

“…The consensus molecular subtype (CMS) is a transcriptome-based system classifying CRC tumors into four subtypes with different characteristics: CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). …”
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Cross Attention-Based Multi-Scale Convolutional Fusion Network for Hyperspectral and LiDAR Joint Classification by Haimiao Ge, Liguo Wang, Haizhu Pan, Yanzhong Liu, Cheng Li, Dan Lv, Huiyu Ma

“…However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. …”
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BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals by S. Premanand, Sathiya Narayanan

“…IntroductionIn recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants have been shown to be effective in the diagnosis of cardiovascular disease from ElectroCardioGram (ECG) signals. …”
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Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset by Nadin Ulrich, Karsten Voigt, Anton Kudria, Alexander Böhme, Ralf-Uwe Ebert

“…To this end, we developed a graph convolutional neural network model (GNN) to predict the water solubility in the form of log S w based on a highly curated dataset of 9800 chemicals. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. …”
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A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks by Guangyu Hou, Zhihui Xin, Guisheng Liao, Penghui Huang, Yuhao Huang, Rui Zou

“…Second, in order to further enhance the representation ability of the model, multiscale information of the target is extracted through different convolution kernel sizes. Finally, the network constructs many complex-valued operation blocks to enhance the robustness of the designed network, including the complex-valued residual block, complex-valued Mish activation function, and complex-valued CBAM. …”
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Peatland pixel-level classification via multispectral, multiresolution and multisensor data using convolutional neural network by Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P. Pitkänen, Sakari Tuominen, Paavo Nevalainen, Jonne Pohjankukka, Jukka Heikkonen

“…These diverse data sources, characterized by different spatial resolutions, are fused to preserve their spatial integrity, enabling richer feature extraction for classification tasks. …”
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Redefining dental image processing: De-convolutional component with residual prolonged bypass for enhanced teeth segmentation by Prasun Kumar, Verma Anil, Mishra Rajiv

“…Early detection and diagnosis can control this rapid growth in dental diseases. Therefore, different traditional techniques are employed for the detection of dental problems. …”
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A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging by Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton

“…We present a deep learning method that utilizes convolutional neural networks (CNNs) to discover trans-Neptunian objects (TNOs) in wide-field survey imaging data. …”
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A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari

“…A Python pipeline was developed using the transfer learning approach, adopting four different convolution neural networks (CNNs). Each architecture (i.e., VGG19, Xception, ResNet50, and DenseNet121) was trained on each of the axial slices of CE-CT images and refined using the validation dataset. …”
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UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. …”
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Fluorescence Spectroscopy and a Convolutional Neural Network for High-Accuracy Japanese Green Tea Origin Identification by Rikuto Akiyama, Kana Suzuki, Yvan Llave, Takashi Matsumoto

“…The fluorescence of each sample was measured in the range of 250 to 550 nm, highlighting the differences in chemical composition that reflect each region. …”
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A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems by Chenlin He, Xiaojun Wang, Jiyu Jiao, Yuhua Huang, Chengpei Han, Yizhuo Zhang, Jianping Zhu

“…The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. …”
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Classification of Short-Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network by Md Hassanuzzaman, Samit Kumar Ghosh, Mohammad Nurul Akhtar Hasan, Mohammad Abdullah Al Mamun, Khawza I. Ahmed, Raqibul Mostafa, Ahsan H. Khandoker

“…This study investigates the minimum signal duration required for accurate automatic classification of heart sounds and evaluates signal quality using the root mean square of successive differences (RMSSD) and the zero-crossing rate (ZCR). …”
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Robust Registration of Multimodal Remote Sensing Images Using Self-Similar Adjacent Self-Convolutional Feature by Tian Gao, Chaozhen Lan, Liang Lv, Qunshan Shi, Wenjun Huang, Yiqiao Wang, Zhizhen Mu

“…Due to significant nonlinear radiometric differences (NRD) and severe geometric distortions (e.g., translation, scale, and rotation) among multimodal images (e.g., optical, infrared, SAR), image registration remains a challenging problem. …”
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Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network by Mengjuan Xu, Xiang Chen, Yuwen Ruan, Xu Zhang

“…The research results of this paper indicate that GNN has certain advantages in overcoming the impact of individual differences, and can be used to provide possible solutions for achieving robust EMG pattern recognition technology.…”
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Convolution Results and Fekete–Szegö Inequalities for Certain Classes of Symmetric q-Starlike and Symmetric q-Convex Functions by Tamer M. Seoudy

“…In this paper, by using the concept of the symmetric q-difference operator, we introduce certain classes of symmetric q-starlike and symmetric q-convex functions. …”
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A Seq-to-Seq Temporal Convolutional Network for Volleyball Jump Monitoring Using a Waist-Mounted IMU by Meng Shang, Camilla de Bleecker, Jos Vanrenterghem, Roel de Ridder, Sabine Verschueren, Carolina Varon, Walter de Raedt, Bart Vanrumste

“…Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. …”
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