HA-CP-Net: A Cross-Domain Few-Shot SAR Oil Spill Detection Network Based on Hybrid Attention and Category Perception by Dongmei Song, Shuzhen Wang, Bin Wang, Weimin Chen, Lei Chen

“…In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. …”
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BREAST-RANKNet: a fuzzy rank-based ensemble of CNNs with residual learning for enhanced breast cancer detection from ultrasound and mammogram images by Sohaib Asif, Lingying Zhu, Dane Yan, Luman Xu, Zhengqiu Huang, Haimin Xu, Ruxuan Yan, Linghong Cai, Changfu Zheng, Jiamei Lin, Enyu Wang

“…Abstract Breast cancer (BC) detection with medical imaging, like ultrasound and mammography, has advanced with deep learning (DL). …”
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Early Detection of Inter-Turn Short Circuits in Induction Motors Using the Derivative of Stator Current and a Lightweight 1D-ResNet by Carlos Javier Morales-Perez, David Camarena-Martinez, Juan Pablo Amezquita-Sanchez, Jose de Jesus Rangel-Magdaleno, Edwards Ernesto Sánchez Ramírez, Martin Valtierra-Rodriguez

“…This work presents a lightweight and practical methodology for detecting inter-turn short-circuit faults in squirrel-cage induction motors under different mechanical load conditions. …”
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A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms by Vikash Singh, Anuj Baral, Roshan Kumar, Sudhakar Tummala, Mohammad Noori, Swati Varun Yadav, Shuai Kang, Wei Zhao

“…This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. …”
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TLEABLCNN: Brain and Alzheimer’s Disease Detection Using Attention-Based Explainable Deep Learning and SMOTE Using Imbalanced Brain MRI by Erol Kina

“…The main aim of this research was to develop a rapid and effective technique for detecting healthy persons before the onset of brain tumours, including AD, pituitary tumours, gliomas, and meningiomas. …”
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NRAP-RCNN: A Pseudo Point Cloud 3D Object Detection Method Based on Noise-Reduction Sparse Convolution and Attention Mechanism by Ziyue Zhou, Yongqing Jia, Tao Zhu, Yaping Wan

“…Moreover, existing methods fail to effectively capture channel correlations and global contextual information during the 2D feature extraction stage after the 3D backbone network, limiting detection performance. To address these challenges, this paper proposes NRAP-RCNN, a pseudo point cloud-based 3D object detection method with two key innovations: (1) A noise-reduction sparse convolution network (NRConvNet), comprising NRConv (noise-resistant submanifold sparse convolution), SRB (sparse convolution residual block), and MHSA (multi-head self-attention). …”
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Phlethysmographic Continuous Detection of Variation of Systemic Blood Pressure during Spinal Anaesthesia in Caesarean Section: A Prospective Observational, Single-centre Study by Sanjay Kumar, Sandeep Khuba, Rafat Shamim, Prabhakar Mishra, Aritra Banerjee, Sikha Khati, Nupur Gupta, Kanika Chaudhary

“…Conclusion: Dicrpleth and PI, both measured from a standard pulse oximetry signal, could be used to detect haemodynamic changes in beat-to-beat manner and guide us to take necessary steps throughout the surgery.…”
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SFTA-Net: a self-supervised approach to detect copy-move and splicing forgery to leverage triplet loss, auxiliary loss, and spatial attention by Amerah Alabrah

“…The previously proposed studies focused on a single type of forgery detection utilizing block-based and key-point feature selection-based classical machine learning (ML) approaches. …”
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Detection of mild cognitive impairment using a virtual reality-based stroop task: a cross-sectional study of embodied behavioral markers by Jin-Hyuck Park

“…Conclusions The VRST provides a valid, reliable, and scalable means of detecting MCI-related executive dysfunction through embodied cognitive-motor interaction. …”
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Non-invasive, non-enzymatic, non-serodiagnostic, and home-detecting paper-based “abnormal UA alarm” for early diagnosis of UA associated diseases by Qian Zhang, Hui’e Jiang, Zhijian Li, Lijuan Chen, Fengqian Yang, Jiamin Zhang, Bo Zhang, Xinhua Liu

“…The NIFS possesses dominantly wide detection range (0–5 000 µmol/L) and high sensitivity (limit of detection = 0.91 µmol/L). …”
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NIRCam Yells at Cloud: JWST MIRI Imaging Can Directly Detect Exoplanets of the Same Temperature, Mass, Age, and Orbital Separation as Saturn and Jupiter by Rachel Bowens-Rubin, James Mang, Mary Anne Limbach, Aarynn L. Carter, Kevin B. Stevenson, Kevin Wagner, Giovanni Strampelli, Caroline V. Morley, Grant Kennedy, Elisabeth Matthews, Andrew Vanderburg, Maïssa Salama

“…Using data from the JWST GO 6122: Cool Kids on the Block program, which targets nearby (<6 pc) M dwarfs with NIRCam coronagraphy and MIRI imaging, we demonstrate that 21 μ m MIRI imaging can detect planets with the same temperature, mass, age, and orbital separations as Saturn and Jupiter. …”
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Fluorescence imaging of bombesin and transferrin receptor expression is comparable to 18F-FDG PET in early detection of sorafenib-induced changes in tumor metabolism. by Jen-Chieh Tseng, Nara Narayanan, Guojie Ho, Kevin Groves, Jeannine Delaney, Bagna Bao, Jun Zhang, Jeffrey Morin, Sylvie Kossodo, Milind Rajopadhye, Jeffrey D Peterson

“…These results suggest that metabolic FLI has potential preclinical application as an additional method for detecting drug-induced metabolic changes in tumors.…”
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Determination of aflatoxin M₁ in milk by ultra-performance liquid chromatography and fluorimetric detection combined with large volume flow cell by WANG Junlin, CAI Zengxuan, REN Yiping

“…In this method, no clear sample solutions were obtained, when passing through the immunoaffinity column, sometimes the immunoaffinity column would be blocked, and the recovery would not in expectation. …”
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Model-Based Deep Network for Single Image Deraining by Pengyue Li, Jiandong Tian, Yandong Tang, Guolin Wang, Chengdong Wu

“…Based on this model, we design a novel channel attention U-DenseNet for rain detection and a residual dense block for rain removal. …”
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StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images by Evren Ekingen, Ferhat Yildirim, Ozgur Bayar, Erhan Akbal, Ilknur Sercek, Abdul Hafeez-Baig, Sengul Dogan, Turker Tuncer

“…StrokeNeXt employs a ConvNeXt‑inspired block and a squeeze‑and‑excitation (SE) unit across four stages: stem, StrokeNeXt block, downsampling, and output. …”
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Research on mulberry leaf disease recognition method based on deep learning by Ye Hui, Xiang Donghui, Zeng Songwei

“…Additionally, a Convolutional Block Attention Module (CBAM) is incorporated into the Neck to highlight key features and regions in the image. …”
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Adaptive building engineering component extraction model based on DSOD by Na Lv, Xuan Yang

“…With the purpose to bring up the extraction efficiency and accuracy of building construction image component information, the dense block structure and loss function were proposed to optimize the deep supervised object detection algorithm, and an adaptive building construction component extraction model based on this algorithm was constructed. …”
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Dam Crack Instance Segmentation Algorithm Based on Improved YOLOv8 by Shuaisen Ma, Mingyue Xu, Weiwu Feng

“…It also employs Diverse Branch Block (DBB) technology to strengthen the convolutional network&#x2019;s ability to extract subtle crack features without increasing inference costs. …”
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Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis by Sanjar Bakhtiyorov, Sabina Umirzakova, Musabek Musaev, Akmalbek Abdusalomov, Taeg Keun Whangbo

“…Background: Brain tumor diagnosis requires precise and timely detection, which directly impacts treatment decisions and patient outcomes. …”
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Multi-Strategy Enhancement of YOLOv8n Monitoring Method for Personnel and Vehicles in Mine Air Door Scenarios by Lei Zhang, Hongjing Tao, Zhipeng Sun, Weixun Yi

“…Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. …”
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