Combining Convolutional Neural Networks for Fungi Classification by Anuruk Prommakhot, Jakkree Srinonchat

“…Hence, this study introduces an innovative and inclusive methodology that utilizes the spatial transformer network technique to analyze fungi thoroughly feature alterations. The fungal characteristics are then subjected to processing by integrating four networks. …”
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Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach by Chandra Wadde, Gayatri Routhu, Mark Clemente-Arenas, Surya Prakash Gummadi, Rupesh Kumar

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Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction by Zhan Guo, Mingxin Lu, Jin Han

“…In this model, a temporal convolutional layer is employed to extract temporal trend features from multivariate topic time series. …”
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Prediction of Potato Rot Level by Using Electronic Nose Based on Data Augmentation and Channel Attention Conditional Convolutional Neural Networks by Jiayu Mai, Haonan Lin, Xuezhen Hong, Zhenbo Wei

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A Drilling Debris Tracking and Velocity Measurement Method Based on Fine Target Feature Fusion Optimization by Jinteng Yang, Yu Bao, Zumao Xie, Haojie Zhang, Zhongnian Li, Yonggang Li

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MLGFENet: Multiscale Local–Global Feature Enhancement Network for High-Resolution Remote Sensing Image Change Detection by Huanhuan Lv, Xianqi Yan, Hui Zhang, Cuiping Shi, Ruiqin Wang

“…The integration of a convolutional neural network (CNN) and a Transformer has become a dominant framework for change detection (CD) in remote sensing images, because of its ability to effectively model both local and global features. …”
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Simulating clinical features on chest radiographs for medical image exploration and CNN explainability using a style-based generative adversarial autoencoder by Kyle A. Hasenstab, Lewis Hahn, Nick Chao, Albert Hsiao

“…Abstract Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. …”
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Super‐resolution reconstruction algorithm for medical images by fusion of wavelet transform and multi‐scale adaptive feature selection by QiaoSu Wang, Qiaomei Ma

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MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images by Miao Wu, Chuanbo Yan, Gan Sen

“…The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. …”
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CAGNet: A Network Combining Multiscale Feature Aggregation and Attention Mechanisms for Intelligent Facial Expression Recognition in Human-Robot Interaction by Dengpan Zhang, Wenwen Ma, Zhihao Shen, Qingping Ma

“…The network integrates the Convolutional Block Attention Module (CBAM) and Global Average Pooling (GAP) modules to optimize the capture of both local and global features. …”
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A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy by Omneya Attallah

“…The results indicate that integrating features from various CNNs and layers markedly improves performance, attaining a maximum accuracy of 99.4%. …”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. …”
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PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model by Zhe Zhou, Hao Wu, Zhenyu Zhang, Bo Kong, Min Yang, Tinghua Ai, Huafei Yu

“…To address this challenge, this study presents a novel model that combines the PNTM with a convolutional neural network (CNN) called PNTM-CNN. In this approach, candidate saddle points are first identified using the PNTM and then refined using a CNN that integrates multiscale topographic features. …”
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PBD-YOLO: Dual-Strategy Integration of Multi-Scale Feature Fusion and Weak Texture Enhancement for Lightweight Particleboard Surface Defect Detection by Haomeng Guo, Zheming Chai, Huize Dai, Lei Yan, Pengle Cheng, Jianhua Yang

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MFSE-TransUNet: A Thyroid Nodule Ultrasound Image Segmentation Network Integrated With Dynamic Feature Calibration and Edge Enhancement by Ye Lu, Jiaojiao Jing, Wenbo Zhang, Yali Kong

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SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation by Yang Zhou Ling Ou, Joon Huang Chuah, Hua Nong Ting, Shier Nee Saw, Jun Zhao

“…Then, an improved Spatial Attention Feature Fusion (SAFF) module is used to enable pixel-level adaptive weighting for optimal local-global feature integration. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

“…This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. …”
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Subdivision of river channel sand micro-scale facies with feature attention spatio-temporal network by Ruipu Zhao, Lili Zeng, Chendong Fu, Xiaoqing Zhao

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Optimized Marine Target Detection in Remote Sensing Images with Attention Mechanism and Multi-Scale Feature Fusion by Xiantao Jiang, Tianyi Liu, Tian Song, Qi Cen

“…The proposed YOLOv5-ASC integrates three core components: an Attention-based Receptive Field Enhancement Module (ARFEM), an optimized SIoU loss function, and a Deformable Convolution Module (C3DCN). …”
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