Technical note: Impact of tissue section thickness on accuracy of cell classification with a deep learning network by Ida Skovgaard Christiansen, Rasmus Hartvig, Thomas Hartvig Lindkær Jensen

“…Method: From HE-stained digitized sections of liver cut manually at 5 thicknesses and on an automated microtome (DS), hepatocytes and non-hepatocytes were manually annotated and loaded into a DL convolutional neural network (ResNet). The network was trained at different settings to identify the thickness with optimal relation between number of training cells and validation accuracy. …”
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DBTU-Net: A Dual Branch Network Fusing Transformer and U-Net for Skin Lesion Segmentation by Wanqing Peng, Jiaojiao Li, Dandan Lai, Qiaomin Lin, Ying Han, Guangrong Huang

“…However, the complexity of skin lesion regions, the ambiguity of their boundaries, and issues such as hair occlusion pose challenges for the segmentation of skin lesions. Currently, models based on the convolutional neural network (CNN) and Transformer have been widely used for the segmentation of skin lesions regions. …”
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FAULT DIAGNOSIS OF GEARBOX UNDER VARIABLE WORKING CONDITION BASED ON WEIGHTED SUBDOMAIN ADAPTIVE ADVERSARIAL NETWORK by ZHANG Huiyun, ZUO Fangjun, YU Xi, YANG Ting

“…To address this issue, a gearbox fault diagnosis method for variable working conditions based on weighted subdomain adaptive adversarial networks was proposed. Initially, a multi-source heterogeneous signal fusion strategy was employed to transform vibration signal spectrograms, current signal Gramian matrices, and infrared thermograms into a multi-channel dataset, offering diverse perspectives on gearbox operational states. …”
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L2-GNN: Graph neural networks with fast spectral filters using twice linear parameterization by Siying Huang, Xin Yang, Zhengda Lu, Hongxing Qin, Huaiwen Zhang, Yiqun Wang

“…To improve learning on irregular 3D shapes, such as meshes with varying discretizations and point clouds with different samplings, we propose L2-GNN, a new graph neural network that approximates the spectral filters using twice linear parameterization. …”
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SAD-Net: a full spectral self-attention detail enhancement network for single image dehazing by Qingjun Niu, Kun Wu, Jialu Zhang, Zhenqi Han, Lizhuang Liu

“…To address the aforementioned issues, we design a new full spectral attention-based detail enhancement dehazing network, named SAD-Net. SAD-Net adopts a U-Net-like structure and integrates Spectral Detail Enhancement Convolution (SDEC) and Frequency-Guided Attention (FGA). …”
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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Met... by A. S. Albahri, Z. T. Al-qaysi, Laith Alzubaidi, Alhamzah Alnoor, O. S. Albahri, A. H. Alamoodi, Anizah Abu Bakar

“…The first category, convolutional neural network (CNN), accounts for 70% (n=21/30). …”
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MSM-TDE: multi-scale semantics mining and tiny details enhancement network for retinal vessel segmentation by Hongbin Zhang, Jin Zhang, Xuan Zhong, Ya Feng, Guangli Li, Xiong Li, Jingqin Lv, Donghong Ji

“…In view of this, we propose a network called multi-scale semantics mining and tiny details enhancement (MSM-TDE). …”
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PC3D-YOLO: An Enhanced Multi-Scale Network for Crack Detection in Precast Concrete Components by Zichun Kang, Kedi Gu, Andrew Yin Hu, Haonan Du, Qingyang Gu, Yang Jiang, Wenxia Gan

“…Our methodology involves three key innovations: (1) the Multi-Dilation Spatial-Channel Fusion with Shuffling (MSFS) module, employing dilated convolutions and channel shuffling to enable global feature fusion, replaces the C3K2 bottleneck module to enhance long-distance dependency capture; (2) the AIFI_M2SA module substitutes the conventional SPPF to mitigate its restricted receptive field and information loss, incorporating multi-scale attention for improved near-far contextual integration; (3) a redesigned neck network (MSCD-Net) preserves rich contextual information across all feature scales. …”
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A Lightweight Dual-Branch Complex-Valued Neural Network for Automatic Modulation Classification of Communication Signals by Zhaojing Xu, Youchen Fan, Shengliang Fang, You Fu, Liu Yi

“…Currently, deep learning has become a mainstream approach for automatic modulation classification (AMC) with its powerful feature extraction capability. …”
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CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China by Tongtong Shi, Ping Wang, Xudong Qi, Jiacheng Yang, Rui He, Jingwen Yang, Yu Han

“…Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. …”
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D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification by Teng Yang, Song Xiao, Jiahui Qu

“…Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification. …”
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A Deep Learning-Based Approach for the Detection of Various Internet of Things Intrusion Attacks Through Optical Networks by Nouman Imtiaz, Abdul Wahid, Syed Zain Ul Abideen, Mian Muhammad Kamal, Nabila Sehito, Salahuddin Khan, Bal S. Virdee, Lida Kouhalvandi, Mohammad Alibakhshikenari

“…Leveraging advanced deep learning methods, specifically Convolutional Neural Networks (CNNs), XIoT analyzes spectrogram images transformed from IoT network traffic data to detect subtle and complex attack patterns. …”
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MDS-Net: An Image-Text Enhanced Multimodal Dual-Branch Siamese Network for Remote Sensing Change Detection by Tao Wang, Tiecheng Bai, Chao Xu, Erlei Zhang, Bin Liu, Xining Zhao, Hongming Zhang

“…Remote sensing change detection (RSCD), which aims to identify differences between bitemporal images, has made great progress through the application of deep learning methods. Convolutional neural networks and transformers are extensively employed in remote sensing image change detection, achieving promising results. …”
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GPC-YOLO: An Improved Lightweight YOLOv8n Network for the Detection of Tomato Maturity in Unstructured Natural Environments by Yaolin Dong, Jinwei Qiao, Na Liu, Yunze He, Shuzan Li, Xucai Hu, Chengyan Yu, Chengyu Zhang

“…The neck network was replaced with the convolutional neural network-based cross-scale feature fusion (CCFF) module to enhance the adaptability of the model to scale changes and to detect many small-scaled objects. …”
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An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images by Md. Romzan Alom, Fahmid Al Farid, Muhammad Aminur Rahaman, Anichur Rahman, Tanoy Debnath, Abu Saleh Musa Miah, Sarina Mansor

“…The proposed model employs Densenet121 as a foundation, integrating customized Convolutional Neural Network (CNN) layers including GlobalAveragePooling2D, Dense, and Dropout layers along with transfer learning to achieve both high accuracy and interpretability for breast cancer diagnosis. …”
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Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with sparsely annotated data by Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen

“…To address these limitations, we propose novel DL methods that can be adopted by any DL architectures—including Convolutional Neural Networks (CNNs), Transformers, or hybrid models—which effectively leverage age and spatial information to enhance model performance. …”
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DCCPNet: A Dual-Branch Channel Cross-Concatenation Pan-Sharpening Network for Satellite Remote Sensing Imagery by Zechun Li, Xunqiang Gong, Ailong Ma, Haiqing He, Pengyuan Lv, Xiansan Liu, Yanfei Zhong

“…Pan-sharpening performance has been significantly improved by deep learning, yet current networks are limited by single-branch architectures or noninteractive dual-branch designs. …”
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Transformer Dil-DenseUnet: An Advanced Architecture for Stroke Segmentation by Nesrine Jazzar, Besma Mabrouk, Ali Douik

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Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics by Tan Qin, Gongxing Yan, Huaguo Jiang, Minqi Shen, Andrea Settanni

“…The proposed DBN/MIGMM model achieves exceptional performance, with an accuracy of 90.189%, specificity of 94.502%, precision of 94.586%, recall of 94.529%, and an F1-score of 88.093%, outperforming state-of-the-art methods such as Fully Convolutional Networks (FCN), You Only Look Once (YOLO), CrackSegNet, Convolutional Neural Networks (CNN), and Convolutional Encoder-Decoder Networks (CedNet). …”
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Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring—A Critical Review by Sibi Chakravathy Parivendan, Kashfia Sailunaz, Suresh Neethirajan

“…We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. …”
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