Rethinking model prototyping through the MedMNIST+ dataset collection by Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann, Christian Ledig

“…Our findings suggest that computationally efficient training schemes and modern foundation models offer viable alternatives to costly end-to-end training. Additionally, we observe that higher image resolutions do not consistently improve performance beyond a certain threshold. …”
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Fish feeding behavior recognition via lightweight two stage network and satiety experiments by Shilong Zhao, Kewei Cai, Yanbin Dong, Guanbo Feng, Yuqing Wang, Hongshuai Pang, Ying Liu

“…Abstract With the advancement of industrial aquaculture, intelligent fish feeding has become pivotal in reducing feed and labor costs while enhancing fish welfare. Computer vision, as a non-invasive and efficient approach, has made significant strides in this domain. …”
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Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema by Anwesa Mondal, Apurba Nandi, Subhasish Pramanik, Lakshmi Kanta Mondal

“…The architecture combines convolutional neural networks (CNNs) for image data with multi-layer perceptron (MLP) for tabular clinical data, allowing for a comprehensive analysis of both data types. …”
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Hot question prediction in Stack Overflow by Li Xian Zhao, Li Zhang, Jing Jiang

“…Besides, the hot question prediction is also helpful for making advertising plan, planning advertising campaigns and estimating costs. Therefore, it is important to predict hot questions. …”
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Real-time dental caries segmentation with an efficient Deformable U-Net (DU-Net) for teledentistry system by Zendi Iklima, Trie Maya Kadarina, Ketty Siti Salamah, Arrival Dwi Sentosa

“…Additionally, AI advancements enhance diagnostic accuracy and streamline clinical decision-making, reducing costs and resource disparities in dental care. This study presents an improved U-Net architecture, Deformable U-Net (DU-Net), for semantic dental caries segmentation, leveraging deformable convolutions to dynamically adjust sampling points for improved feature extraction and reduced computational redundancy. …”
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Multi-model Approach for Tree Detection and Classification in Wallonia Region (Belgium) by N. Dimarco, B. Bartiaux, L. Andreani, R. Schlögel

“…These findings highlight the potential of multi-temporal aerial imagery and deep learning to enhance forest inventories, reduce field survey costs, and inform targeted management.…”
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Optimization of Coverage Path Planning for Agricultural Drones in Weed-Infested Fields Using Semantic Segmentation by Fabian Andres Lara-Molina

“…First, semantic segmentation is performed using a DeepLab v3+ convolutional neural network to identify and classify regions containing weeds based on aerial imagery. …”
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Damage Detection Method for Road Ancillary Facilities Integrating Attention Mechanism by Shuang Yang, Huiqin Wang, Ke Wang, Nan Guo

“…The model first introduces the D-GhostNet V3Conv module, replacing the standard convolutional layers, significantly enhancing feature extraction capabilities while reducing computational costs. …”
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Autonomous UAV Detection of <i>Ochotona curzoniae</i> Burrows with Enhanced YOLOv11 by Huimin Zhao, Linqi Jia, Yuankai Wang, Fei Yan

“…To address the inefficiency and high costs of traditional pika burrow monitoring, this study proposes an intelligent monitoring solution that integrates drone remote sensing with deep learning. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…Recent trends in transformer fault diagnosis include the use of advanced monitoring systems and data analysis techniques, and implementing effective FDD strategies can prevent costly repairs, minimize downtime, and enhance the overall reliability of power systems. …”
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Strawberry Disease Detection Using Multispectral UAV Imagery by A. F. Cheshkova, V. S. Riksen

“…Accurate, timely, and non-invasive diagnosis of plant diseases is essential in the industrial cultivation of strawberries, as it helps minimize yield losses and reduce treatment costs. With the advancement of unmanned aerial vehicles and sensor technologies, remote sensing has emerged as a promising tool for monitoring crop health and detecting diseases. …”
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A Study on the STGCN-LSTM Sign Language Recognition Model Based on Phonological Features of Sign Language by Yuxin Han, Yong Han, Qi Jiang

“…The general extraction and recognition of sign language features can result in confusion when distinguishing similar words, and fine-grained feature extraction of sign language actions may incur high computational costs. To deal with these challenges, this paper proposes a dual-stream deep learning model built on Spatio-Temporal Graph Convolutional Network-Long Short-Term Memory(STGCN-LSTM), which aims to capture both the local features of sign language and the global spatio-temporal characteristics of sign words. …”
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach by Sumaira Tabassum, M. Jawad Khan, Javaid Iqbal, Asim Waris, M. Adeel Ijaz

“…Identifying anomalies is often costly, time-consuming, heavily reliant on expert interpretation, and requires considerable manual effort. …”
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Gas pipeline leakage detection based on multiple multimodal deep feature selections and optimized deep forest classifier by Eddie Zhang, Evan Zhang

“…Then a deep feature pool is constructed using the selected Convolutional Neural Network (CNN) models, including AlexNet, ResNet-50, MobileNet, VggNet, and EfficientNet, as well as their inner layers. …”
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Co-occurrence feature learning for visual recognition of immature leukocytes by Yi-Ting Hsiao, Si-Wa Chan, Yen-Chieh Ouyang, Ju-Huei Chien

“…Manual examination of blood smear images for leukemia diagnosis is often laborious and costly. Computer-aided classification of WBCs has the potential to assist hematologists in improving diagnostic accuracy. …”
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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images by Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho, Byoungchol Chang

“…Two novel architectures, Sparse Convolutional DenseNet201 with Self-Attention (SC-DSAN) and CNN-GRU, are fused at the network level using a depth concatenation layer, avoiding the computational costs of feature-level fusion. …”
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Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…Accurate prediction of wind power generation (WPG) under real-world scenarios is imperative for achieving optimal costing, ensuring reliable operation, and fortifying the security of power systems. …”
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Commercial Biomaterial-Based Products for Tendon Surgical Augmentation: A Scoping Review on Currently Available Medical Devices by Marta Pluchino, Leonardo Vivarelli, Gianluca Giavaresi, Dante Dallari, Marco Govoni

“…However, scientific innovations must navigate convoluted clinical regulatory paths, which, due to high costs for investors, long development timelines, and funding shortages, hinder the translation of many scientific discoveries into routine clinical practice.…”
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Computer Vision in Monitoring Fruit Browning: Neural Networks vs. Stochastic Modelling by Maria Kondoyanni, Dimitrios Loukatos, Charalampos Templalexis, Diamanto Lentzou, Georgios Xanthopoulos, Konstantinos G. Arvanitis

“…As human labour is limited and therefore expensive, computer vision has emerged as a solution with encouraging results for monitoring and sorting tasks in the agrifood sector, where conventional methods for inspecting fruit browning that are generally subjective, time-consuming, and costly. Thus, this study investigated the application of computer vision techniques and various RGB cameras in the detection and classification of enzymatic browning in cut pears, comparing convolutional neural networks (CNNs) with stochastic modelling. …”
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Reconstruction, Segmentation and Phenotypic Feature Extraction of Oilseed Rape Point Cloud Combining 3D Gaussian Splatting and CKG-PointNet++ by Yourui Huang, Jiale Pang, Shuaishuai Yu, Jing Su, Shuainan Hou, Tao Han

“…Manual phenotyping not only consumes a lot of labor and time costs, but even the measurement process can cause structural damage to oilseed rape plants. …”
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