SeaConvNeXt: A Lightweight Two-Branch Network Architecture for Efficient Prediction of Specific IHC Proteins and Antigens on Hematoxylin and Eosin (H&E) Images by Yuli Chen, Guoping Chen, Guoying Shi, Yao Zhou, Jiayang Bai, Germán Corredor, Cheng Lu, Xiujuan Lei

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LSTM time series prediction of soil moisture content in kiwifruit root zone based on meteorological data fusion by Jingyuan He, Weifeng Li, Shijia Pan, Nikolaos Sygrimis, Zijie Niu, Dongyan Zhang, Dong Han, Petro A. Roussos

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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

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Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data by Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu

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Terrain and Atmosphere Classification Framework on Satellite Data Through Attentional Feature Fusion Network by Antoni Jaszcz, Dawid Połap

“…Surface, terrain, or even atmosphere analysis using images or their fragments is important due to the possibilities of further processing. …”
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Photodiagnosis with deep learning: A GAN and autoencoder-based approach for diabetic retinopathy detection by Kerem GENCER, Gülcan GENCER, Tuğçe Horozoğlu CERAN, Aynur ER BİLİR, Mustafa DOĞAN

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Improving long‐tail classification via decoupling and regularisation by Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Wenjian Luo, Peiyi Han, Qing Liao, Guandong Xu

“…Abstract Real‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…Abstract In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer architecture, crafted to adeptly handle 256 × 256 pixel images across three color channels (RGB). …”
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CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery by Wenqing Liu, Hongtao Huo, Luyan Ji, Yongchao Zhao, Xiaowen Liu, Jialei Xie

“…Second, considering the unique horizontal characteristics exhibited by the PV panels in remote sensing images, a south-facing orientation prior module is designed to enhance the horizontal features and improve our network in capturing horizontal objects. …”
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Advances in Neural Network assisted Tool Pressure Prediction by Göltl Florian, Harst Felix, Birkert Arndt, Stache Nicolaj C.

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Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar by Yukun Gao, Lin Cao, Zongmin Zhao, Dongfeng Wang, Chong Fu, Yanan Guo

“…However, labeling a large number of radar datasets is difficult and time-consuming, and it is difficult for models trained on insufficient labeled data to obtain exact classification results. In this paper, we propose a multiscale residual weighted classification network with large-scale, medium-scale, and small-scale residual networks. …”
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What Helps to Detect What? Explainable AI and Multisensor Fusion for Semantic Segmentation of Simultaneous Crop and Land Cover Land Use Delineation by Saman Ebrahimi, Saurav Kumar

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Oriented ice eddy detection network based on the Sentinel-1 dual-polarization data by Jinqun Wu, Jinqun Wu, Yiqin Zheng, Tingting Wang, Chunyong Ma, Chunyong Ma, Ge Chen, Ge Chen

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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

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The Teacher–Assistant–Student Collaborative and Competitive Network for Brain Tumor Segmentation with Missing Modalities by Junjie Wang, Huanlan Kang, Tao Liu

“…Each modality offers distinct contrast and tissue characteristics, which help in the more comprehensive identification and analysis of tumor lesions. However, in clinical practice, only a single modality of medical imaging is available due to various factors such as imaging equipment. …”
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Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Pennisetum... by Faten Dhawi, Abdul Ghafoor, Norah Almousa, Sakinah Ali, Sara Alqanbar

“…Smartphone-based RGB imaging was used for data collection, and Shapley additive explanations (SHAP) methodology evaluated predictor importance. …”
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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…Our findings demonstrated the potential of data-driven models trained on hyperspectral imaging data for non-destructive assessment of hemp seed vigor. …”
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CRISP: correlation-refined image segmentation process by Jennifer K. Briggs, Erli Jin, Matthew J. Merrins, Richard K. P. Benninger

“…Results CRISP cell mask refinement had an area under the receiver operating curve of 0.835, indicating good model performance on the training data set. CRISP had 77% accuracy when testing on a separate data set, which came from a different mouse model imaged with a different microscope than the training data set. …”
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Interdisciplinary approaches to image processing for medical robotics by Ludan Chen, Shiwen Wu, Stephen C. H. Leung

“…This study explores the interdisciplinary physics underlying image fusion and analysis, addressing challenges such as integrating complementary features, handling dynamic range variations, and suppressing noise in real-world medical contexts.MethodsWe introduce the Multi-Scale Feature Adaptive Fusion Network (MFAFN) and the Dynamic Feature Refinement Strategy (DFRS), which leverage principles from computational and experimental physics to enhance imaging techniques. …”
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