Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks by Andrzej Liebert, Dominique Hadler, Chris Ehring, Hannes Schreiter, Luise Brock, Lorenz A. Kapsner, Jessica Eberle, Ramona Erber, Julius Emons, Frederik B. Laun, Michael Uder, Evelyn Wenkel, Sabine Ohlmeyer, Sebastian Bickelhaupt

“…Key Points Breast MRI T2w-fat-saturated (FS) images can be virtually generated using convolutional neural networks. Image blurring in virtual T2w-FS images currently limits their clinical applicability. …”
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Study of the Current–Voltage Characteristics of Membrane Systems Using Neural Networks by Evgenia Kirillova, Anna Kovalenko, Makhamet Urtenov

“…This article is dedicated to the construction of neural networks for the prediction of the current–voltage characteristic (CVC). …”
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Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network by Zhongmei Wang, Pengxuan Nie, Jianhua Liu, Jing He, Haibo Wu, Pengfei Guo

“…Then, the spatial aggregation property of Graph Convolutional Neural Networks (GCN) is utilized to capture the dependency relationship between current and vibration modes with similar time step features for accurately fusing contextual semantic information. …”
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Advancements and outlooks in utilizing Convolutional Neural Networks for plant disease severity assessment: A comprehensive review by Douglas Leite, Alisson Brito, Gregorio Faccioli

“…Once limited to disease detection, Convolutional Neural Networks (CNNs) applications now exhibit automatic severity calculation and classification solutions. …”
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Diagnosis of Commutation Failure in a High- Voltage Direct Current Transmission System Based on Fuzzy Entropy Feature Vectors and a PCNN-GRU by Cao Ruirui, Yang Taigang, Li Guohui, Chen Shilong

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Distributed Photovoltaic Distribution Voltage Prediction Based on eXtreme Gradient Boosting and Time Convolutional Networks by Fang Yuan, Yong Lu, Zhi Xie, Shenxiang Dai

“…To address this, the study proposes a distributed photovoltaic distribution voltage prediction model based on gradient boosting tree and time convolutional network. The model uses eXtreme gradient boosting for feature selection and time convolutional network and two-layer prediction strategy for voltage prediction. …”
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Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints by Fenghui Lian, Yingjie Sun, Meiyu Li

“…Specifically, for the auxiliary constraint, a set of convolutional structures are involved into a conventional network to act as a discriminator, then adversarial network is established. …”
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Transformer and Convolutional Neural Network: A Hybrid Model for Multimodal Data in Multiclass Classification of Alzheimer’s Disease by Abdulaziz Alorf

“…This study uses rs-fMRI, clinical data, and transformer-based models to classify six AD stages. The proposed network is a hybrid of two architectures, namely a transformer and a convolutional neural network (CNN). …”
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Identification and Classification of Atmospheric Particles Based on SEM Images Using Convolutional Neural Network with Attention Mechanism by Changchang Yin, Xuezhen Cheng, Xilu Liu, Meng Zhao

“…Thirdly, by introducing attention mechanism into convolutional neural network, an Attention-CNN model for the identification and classification of the four types of atmospheric particles based on the SEM images is established. …”
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Attention Adaptive Temporal Graph Convolutional Network for Long-Term Seasonal Sea Surface Temperature Forecasting by Ling Xiao, Peihao Yang, Yuxue Wang, Sheng Li, Baoqin Chen

“…To address this, we introduce a novel attention adaptive temporal graph convolutional network (AA-TGCN) specifically designed for long-term seasonal SST forecasting. …”
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Enhanced plant disease classification with attention-based convolutional neural network using squeeze and excitation mechanism by S. Karthikeyan, R. Charan, Sathiya Narayanan, L. Jani Anbarasi

“…These tools help boost productivity by automating tasks and allowing real-time analysis on devices with limited memory and resources. However, many current models struggle with accuracy, size, and speed particularly when handling multi-label classification problems.MethodsThis paper proposes a Convolutional Neural Network with Squeeze and Excitation Enabled Identity Blocks (CNN-SEEIB), a hybrid CNN-based deep learning architecture for multi-label classification of plant diseases. …”
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iPro-CSAF: identification of promoters based on convolutional spiking neural networks and spiking attention mechanism by Qian Zhou, Jie Meng, Hao Luo

“…In this study, iPro-CSAF, a convolutional spiking neural network combined with spiking attention mechanism is designed for promoter recognition. …”
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ScarpLearn: an automatic scarp height measurement of normal fault scarps using convolutional neural networks by Léa Pousse-Beltran, Theo Lallemand, Laurence Audin, Pierre Lacan, Andres David Nunez-Meneses, Sophie Giffard-Roisin

“…We developed a Bayesian supervised machine learning method using one-dimentional (1D) convolutional neural networks (CNN) trained on a database of simulated topographic profiles across normal fault scarps, called ScarpLearn. …”
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A Dual-Branch Network of Strip Convolution and Swin Transformer for Multimodal Remote Sensing Image Registration by Kunpeng Mu, Wenqing Wang, Han Liu, Lili Liang, Shuang Zhang

“…At the same time, we developed a strategy to parallelize various convolution kernels in affine parameter regression networks, aiming to enhance the accuracy of transformation parameters and the robustness of the model. …”
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Early breast cancer detection in CT scans using convolutional neural bidirectional feature pyramid network by Tahani Jaser Alahmadi, Adeel Ahmed, Amjad Rehman, Abeer Rashad Mirdad, Bayan Al Ghofaily, Shehryar Ali

“…In this article, we proposed a framework called convolutional neural bidirectional feature pyramid network, which integrates multi-scale feature extraction and bidirectional feature fusion for breast cancer detection in CT scans. …”
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Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning by Enock Adjei Agyekum, Zhang Yuzhi, Yu Fang, Doris Nti Agyekum, Xian Wang, Eliasu Issaka, CuiRong Li, Xiangjun Shen, Xiaoqin Qian, Xinping Wu

“…Abstract This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). …”
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Resistance Welding Quality Through Artificial Intelligence Techniques by Luis Alonso Domínguez-Molina, Edgar Rivas-Araiza, Juan Carlos Jauregui-Correa, Jose Luis Gonzalez-Cordoba, Jesús Carlos Pedraza-Ortega, Andras Takacs

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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…We present the development of an artificial intelligence computer vision process using a deep convolutional neural network to detect acid-fast bacilli (AFB) from Kinyoun acid-fast stained slides. …”
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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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Papillary Thyroid Carcinoma Semantic Segmentation Using Multi-Scale Adaptive Convolutional Network With Dual Decoders by Thanat Payatsuporn, Pittipol Kantavat, Nichthida Tangnuntachai, Nopporn Tipparawong, Waratchanok Techapapa, Boonserm Kijsirikul, Somboon Keelawat

“…This study proposes Multi-scale Adaptive Convolutional Network with DBUNet (MSAC-DBUNet) and Multi-scale Adaptive Convolutional Network with Dual Decoders (MSAC-DD), which are more accurate and faster than the traditional networks. …”
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