Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of... by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven, Rebecca Retzlaff

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Deep Learning-Assisted Diagnostic System: Apices and Odontogenic Sinus Floor Level Analysis in Dental Panoramic Radiographs by Pei-Yi Wu, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Chiung-An Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R. Abu

“…This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. …”
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A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Raja Rizal Azman Bin Raja Aman, Nadia Fareeda Muhammad Gowdh, Izzah Amirah Binti Mohd Ahir, Mohd Salahuddin Bin Kamaruddin, Khairul Azmi Abdul Kadir, Filippo Molinari, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…This study introduces a novel dual-stream deep learning framework for ischemic stroke classification using Computed Tomography (CT) images, specifically addressing challenges in accuracy, computational efficiency, and clinical interpretability. …”
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On Risk Assessment for Out-of-Distribution Detection by Anton Vasiliuk

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The Bayesian mixture expert recognition model for tobacco leaf curing stages based on feature fusion by Panzhen Zhao, Shijiang Duan, Songfeng Wang, Aihua Wang, Lingfeng Meng, Zhicheng Wang, Yingpeng Dai

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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 experiments followed 10 runs of the five-fold cross-validation process on a total of 1820 ultrasound images and the results were compared using Wilcoxon signed-rank test. …”
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A novel NDSM fusion approach to improve UAV-Based LCC accuracy in sloping urban areas by Umut Gunes Sefercik, Ilyas Aydin, Mertcan Nazar, Can Atalay, Taskin Kavzoglu

“…To preserve the information provided by the NDSM data for each image object, a single-stage segmentation process has been performed in contrast to BU segmentation. …”
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Hybrid 3B Net and EfficientNetB2 Model for Multi-Class Brain Tumor Classification by R. Preetha, M. Jasmine Pemeena Priyadarsini, J. S. Nisha

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Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy by Tobias Schraut, Anne Schützenberger, Tomás Arias-Vergara, Melda Kunduk, Matthias Echternach, Stephan Dürr, Julia Werz, Michael Döllinger

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CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading by Qinglin He, Xiaobing Zhang, Jianxin Hu, Zehua Sheng, Qi Li, Si-Yuan Cao, Hui-Liang Shen

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Improving AI performance in wildlife monitoring through species and environment-specific training: A case study on desert Bighorn sheep by Owen S. Okuley, Christina M. Aiello, Will Glad, Kyle Perkins, Richard Ianniello, Neal Darby, Clinton W. Epps

“…Artificial intelligence (AI) models can expedite image processing, but automated species classifications can be too inaccurate to meet end-users' needs. …”
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Explainable handcrafted features for mitotic event detection and classification by Panason Manorost, Thomas Deckers, Veerle Bloemen, Jean Marie Aerts

“…These unwanted noise elements and artifacts can cause false positive detections and lead to low precision in detecting proliferation processes. Additionally, traditional cell imaging methods are based on single-cell segmentation resulting in lower performance for high cell densities. …”
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DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…However, traditional maize seed classification methods mainly rely on single modal data, which limits the accuracy and robustness of classification. …”
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Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li, Xiaoqiong Jiang

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STUDY ON DEEP LEARNING MODELS FOR THE CLASSIFICATION OF VR SICKNESS LEVELS by Haechan NA, Yoon Sang KIM

“…The proposed model consists of a visual processing module, a motion processing module, and an FC-based VR sickness level classification module. …”
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Fusion of microscopic and diffraction images with VGG net for budding yeast recognition in imaging flow cytometry by Yangguang Han, Qifeng Li, Pengpeng Sun, Xiangyun Ma, Yunpeng Yang, Ning Zhang, Jingwen Feng, Yu Sa

“…However, MDIFC is still hindered by challenges related to limited accuracy, processing speed, and a lack of automation. To address this, we propose a novel approach that integrates image fusion techniques with a deep learning-based classification algorithm. …”
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Estimating building height using scene classification and spatial geometry by Yonghua Jiang, Jingxin Chang, Yunming Wang, Shaodong Wei, Deren Li

“…This study introduces a novel method for estimating building height by integrating scene classification with spatial geometric relationships. Initially, raw data are processed to derive the various data types required for this approach. …”
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Object-Specific Multiview Classification Through View-Compatible Feature Fusion by Javier Perez Soler, Jose-Luis Guardiola, Nicolás García Sastre, Pau Garrigues Carbó, Miguel Sanchis Hernández, Juan-Carlos Perez-Cortes

“…In these scenarios, relying on a single image may not provide sufficient information to effectively identify the scrutinized object, as different perspectives may reveal distinct characteristics that are essential for accurate classification. …”
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Cardiac Disorder Classification by Electrocardiogram Sensing Using Deep Neural Network by Ali Haider Khan, Muzammil Hussain, Muhammad Kamran Malik

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The Use of Fractal Features from the Periphery of Cell Nuclei as a Classification Tool by Birgitte Nielsen, Fritz Albregtsen, Håvard E. Danielsen

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