Network analysis of an OSCE-based graduation skills assessment for clinical medical students by Huiqun Zhang, Shanshan Li, Guoquan Zheng, Xiaoyun Chen, Lujie Zhong, Jiaying Li, Yun Li

“…Methods Analyzing data from 3,710 students across three medical specialities (clinical medicine, anesthesiology, medical imaging) from a leading medical institution in Guangzhou, China (Grade 2011–2016), and the independent samples t-tests, analysis of variance (ANOVA), Pearson’s correlation, and network analysis were employed to dissect performance trends, compare demographic group differences, and unearth the correlations among the examination modules. …”
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Research on Automatic Identification Method of Loess Earthquake Landslide Based on Remote Sensing Imagery by WANG Liansheng, LI ping, MU SongWei, TUO Zihan, Li Chen

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Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods by Hongran Li, Nuo Wang, Zixuan Du, Deyu Huang, Mengjie Shi, Zhaoman Zhong, Dongqing Yuan

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A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face Recognition by Andisani Nemavhola, Colin Chibaya, Serestina Viriri

“…The databases span from early datasets like Olivetti Research Laboratory (ORL) and Facial Recognition Technology (FERET) to more recent collections such as MegaFace and Ms-Celeb-1M, offering a range of sizes, subject diversity, and image quality. Older databases, such as ORL and FERET, are smaller and cleaner, while newer datasets enable large-scale training with millions of images but pose challenges like inconsistent data quality and high computational costs. …”
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Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and Segmentation by Ojonugwa Oluwafemi Ejiga Peter, Opeyemi Taiwo Adeniran, Adetokunbo MacGregor John-Otumu, Fahmi Khalifa, Md Mahmudur Rahman

“…., DALL-E 2, Vector-Quantized Generative Adversarial Network (VQ-GAN)) have been used to generate images but not colonoscopy data for intelligent data augmentation. …”
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Combined Application of Deep Learning and Radiomic Features for Classification of Lung CT Images by Shariati Faridoddin, V. A. Pavlov

“…The use of a convolutional neural network enabled large volumes of data to be processed, surpassing the performance of conventional methods. …”
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Detecting Alzheimer's Based on MRI Medical Images by Using External Attention Transformer by Farrel Ardannur Deswanto, Isman Kurniawan

“…Unfortunately, the traditional method of detecting Alzheimer's has several limitations, such as subjective analysis and delayed diagnosis. One commonly used method is visual inspection, which uses magnetic resonance imaging (MRI). …”
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Classification of maize seed hyperspectral images based on variable-depth convolutional kernels by Yating Hu, Hongchen Zhang, Hongchen Zhang, Changming Li, Qianfu Su, Wei Wang

“…The method offers a promising framework for hyperspectral image analysis in seed classification and other agricultural applications.…”
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A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing by Jiayuan Shen, Bing Han, Yang Li, Zongxu Pan, Di Yin, Yugang Feng, Guangzuo Li

“…Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. …”
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Mapping the research landscape of PET/CT in lymphoma: insights from a bibliometric analysis by Die Zhang, Jianding Peng, Yingjie Zhu, Qiang Gong, Qing Wang, Chaodong Xiang, Hanjian Du, Xiaofei Hu

“…ObjectiveThis study provides a comprehensive bibliometric analysis of research trends in Positron Emission Tomography/Computed Tomography (PET/CT) applications for lymphoma, aiming to identify key contributors, emerging topics, and collaboration patterns within the field.MethodsData from the Web of Science Core Collection (2004–2024) were analyzed. …”
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A Deep Learning Approach for Diabetic Retinopathy Classification Using Retinal Images by Rathod-Jadhav Kavita, Pande Aparna

“…The folding neural network (CNN) model architecture analyzes retinal images and classifies them with high accuracy as diabetic or non-diabetic classification. …”
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A Deep Learning-Based Approach for Cell Segmentation in Phase-Contrast Images by Basma A. Mohamed, Nancy M. Salem, Walid Al-Atabany, Lamees N. Mahmoud

“…Cell segmentation in Phase-Contrast (PhC) microscopy is crucial for non-invasive analysis of live-cell imaging data. However, traditional segmentation methods often struggle with challenges such as low contrast, overlapping cells, and artifacts that are unique to PhC imaging. …”
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Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net by Jinlong Wu, Xin Wu, Ronghui Miao

“…It can also provide reference for multispectral data analysis and semantic segmentation in the field of minor grain crops.…”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. Object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. …”
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Using publicly available data for predicting socioeconomic values in urban context by Maximiliano Ojeda, Juan Reutter

“…We leverage Graph Neural Network (GNN) models to capture the spatial relationships inherent in network data while integrating perceptual features extracted from images to enhance predictive accuracy. …”
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Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images by Arezoo Borji, Arezoo Borji, Arezoo Borji, Gernot Kronreif, Bernhard Angermayr, Bernhard Angermayr, Sepideh Hatamikia, Sepideh Hatamikia

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Bimodal data analysis for early detection of lameness in dairy cows using artificial intelligence by Yashan Dhaliwal, Hangqing Bi, Suresh Neethirajan

“…To address the urgent need for early detection, we introduce a novel bimodal artificial intelligence (AI) framework that leverages both facial biometric data and accelerometer-based movement metrics. Over a 21-day period, six Holstein cows were monitored to capture variations in facial expressions and locomotion, and a multimodal model was built by combining DenseNet-121 for image analysis with Long Short-Term Memory (LSTM) networks for time-series data. …”
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DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model by Zhe Chen, Guohao Xie, Xiaofang Deng, Jie Peng, Hongbing Qiu

“…Affected by the complex underwater environment and the limitations of low-resolution sonar image data and small sample sizes, traditional image recognition algorithms have difficulties achieving accurate sonar image recognition. …”
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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…Automated AML subtype detection is especially important for underrepresented subtypes to ensure equitable diagnostics; (2) Methods: This study explores the potential of ConvNeXt, an advanced convolutional neural network architecture, for classifying high-resolution peripheral blood smear images into AML subtypes. …”
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Characterizing Breast Tumor Heterogeneity Through IVIM-DWI Parameters and Signal Decay Analysis by Si-Wa Chan, Chun-An Lin, Yen-Chieh Ouyang, Guan-Yuan Chen, Chein-I Chang, Chin-Yao Lin, Chih-Chiang Hung, Chih-Yean Lum, Kuo-Chung Wang, Ming-Cheng Liu

“…The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). …”
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