Convolutional neural network prediction of the particle size distribution of soil from close-range images by Enrico Soranzo, Carlotta Guardiani, Wei Wu

“…In this study, we propose a convolutional neural network approach for predicting the particle size distribution using soil image analysis. …”
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Two-Stream Bidirectional Interaction Network Based on RGB-D Images for Duck Weight Estimation by Diqi Zhu, Shan Bian, Xiaofeng Xie, Chuntao Wang, Deqin Xiao

“…For the experimental analysis of this study, we built a new dataset of RGB-D duck images consisting of 2865 pairs of RGB-D images captured from the bird-eye view. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…Spectral data and textural features were extracted from multispectral images of broccoli and fused with the physicochemical parameters for analysis. …”
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Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images by Lubna Farhi, Razia Zia, Zain Anwar Ali

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Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images by Wenbo Pang, Yi Ma, Huiyan Jiang, Qiming Yu

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MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities by Zeki Kuş, Musa Aydin

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Spatial-Spectral Linear Extrapolation for Cross-Scene Hyperspectral Image Classification by Lianlei Lin, Hanqing Zhao, Sheng Gao, Junkai Wang, Zongwei Zhang

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Deep neural network modeling for brain tumor classification using magnetic resonance spectroscopic imaging. by Erin B Bjørkeli, Knut Johannessen, Jonn Terje Geitung, Anna Karlberg, Live Eikenes, Morteza Esmaeili

“…Our proposed model, which utilizes deep neural networks, is specifically designed for the analysis and classification of spectral time series data. …”
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Color Night-Light Remote Sensing Image Fusion With Two-Branch Convolutional Neural Network by Jie Wang, Yanling Lu, Yuefeng Wang, Jianwu Jiang

“…To address the low-resolution limitation of NLRSI, this study proposes a multisource remote sensing image fusion framework based on the two-branch convolutional neural network (TbCNN), which fuses Landsat-8 and NPP/VIIRS data to generate high-resolution color night-light remote sensing imagery (CNLRSI). …”
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Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection by Hanchi Liu, Jinrong He, Yanxin Shi, Yingzhou Bi

“…To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. …”
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Image-based yield prediction for tall fescue using random forests and convolutional neural networks by Sarah Ghysels, Bernard De Baets, Dirk Reheul, Steven Maenhout

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MCPA: multi-scale cross perceptron attention network for 2D medical image segmentation by Liang Xu, Mingxiao Chen, Yi Cheng, Pengwu Song, Pengfei Shao, Shuwei Shen, Peng Yao, Ronald X. Xu

“…Abstract The UNet architecture, based on convolutional neural networks (CNN), has demonstrated its remarkable performance in medical image analysis. …”
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Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou, Chao Xin

“…MDSIS-Net is tested on clinical disfiguring dermatosis data and the public Derm7pt melanoma dataset. A Visually Intelligent System for Image Analysis (VISIA) captures five modalities: spots, red marks, ultraviolet (UV) spots, porphyrins, and brown spots for disfiguring dermatosis. …”
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Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis by Saif Agha, Eric Psota, Simon P. Turner, Craig R. G. Lewis, Juan Pedro Steibel, Andrea Doeschl-Wilson

“…This proof-of-concept study addresses, for the first time, the hypothesis that applying social network analysis (SNA) on AI-automated monitoring data could potentially facilitate the analysis of social structures of farm animals. …”
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Aircraft Recognition Based on CNN Using Satellite Images by Meriç GENC, Yıldıray YALMAN

“…This study examines aircraft recognition using Convolutional Neural Networks (CNN) with satellite-derived image data. The research traces the evolution of deep learning, emphasizing the importance of multi-layer neural networks in addressing the limitations of artificial intelligence. …”
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A Novel Deeply-Learned Image Quality Analysis Algorithm for Clustering by Zhongzhe Chen, Xing Gao

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Bladder volume estimation based on USG images by Volodymyr Mosorov, Daniel Baradziej, Marta Chodyka

“…The research employs Convolutional Neural Networks (CNNs) and the MONAI platform for image segmentation and analysis, using data from The Cancer Imaging Archive to focus on urological regions. …”
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A Systematic Analysis of Neural Networks, Fuzzy Logic and Genetic Algorithms in Tumor Classification by Ahmed Al-Ashoor, Ferenc Lilik, Szilvia Nagy

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Advanced Artificial Intelligence Techniques for Comprehensive Dermatological Image Analysis and Diagnosis by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…This paper explores how AI, particularly Convolutional Neural Networks (CNNs), can enhance RCM image analysis, emphasizing machine learning (ML) and deep learning (DL) methods that improve diagnostic accuracy and efficiency. …”
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Image Clustering based on Artificial Intelligence Techniques by Baydaa Khaleel

“…We have take the advantage of classification abilities of Artificial Intelligence Techniques (AITs) to classify images data set into a number of clusters. The Gath-Geva (GG) fuzzy clustering algorithm, Artificial Bee Colony algorithm(ABC), Radial Basis Function Network(RBF), and then combined Gath-Geva algorithm with (RBF) algorithm to produce Fuzzy RBF (FRBF) method were applied using images data set to classify this data set into a number of clusters (classes). …”
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