Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…The presented study aims to investigate discharging and arc spectra on solar cell surfaces through the analysis of arc images. Leveraging advanced Deep Learning (DL) methodologies, including Convolutional Neural Networks (CNN) and Transfer Learning, a robust predictive model has been developed to analyze arc behavior and identify defective cells based on image data. …”
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Imaging of brain electric field networks with spatially resolved EEG by Lawrence R Frank, Vitaly L Galinsky, Olave Krigolson, Susan Tapert, Stephan Bickel, Antigona Martinez

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DPA-HairNet: A Dual Encoder Attention Based Network for Hair Artifact Removal in Dermoscopic Images by F M Javed Mehedi Shamrat, Mohd Yamani Idna Idris, Chowdhury Forhadul Karim, Xujuan Zhou, Raj Gururajan

“…These results underscore DPA-HairNet’s effectiveness and potential integration into clinical workflows to enhance dermoscopic image analysis and diagnosis. Future work will explore generalization to additional artifact types and optimization for real-time clinical deployment.…”
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Cross-sensor data reconstruction for optical remote sensing gap-filling with attention-enhanced multi-scale fusion network by Xi Wang, Songchao Chen, Chang Zhou, Si-Bo Duan, Zhou Shi

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A generative benchmark for evaluating the performance of fluorescent cell image segmentation by Jun Tang, Wei Du, Zhanpeng Shu, Zhixing Cao

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Topological data analysis of human brain networks through order statistics. by Soumya Das, D Vijay Anand, Moo K Chung

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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…In this paper, we propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data derived from magnetic resonance imaging. …”
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Analysis of the PointNet neural network architecture by L. A. Shchenyavskaya, D. A. Gura, R. A. Dyachenko

“…Most researchers convert point cloud data into ordinary three-dimensional voxel grids or image collections, which makes the data unnecessarily voluminous and causes problems when processing them. …”
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Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data by Jaqueline Alvarenga Silveira, Alexandre Ray da Silva, Mariana Zuliani Theodoro de Lima

“…SVMs are effective with high-dimensional clinical data, while Neural Networks in genetic and molecular analysis. …”
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Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging by Chiara Manini, Markus Hüllebrand, Lars Walczak, Sarah Nordmeyer, Lina Jarmatz, Titus Kuehne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Jennifer Erley, Sebastian Kelle, Peter Bannas, Ralf Felix Trauzeddel, Jeanette Schulz-Menger, Anja Hennemuth

“…ABSTRACT: Background: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. …”
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TransTab: A transformer-based approach for table detection and tabular data extraction from scanned document images by Yongzhou Wang, Wenliang Lv, Weijie Wu, Guanheng Xie, BiBo Lu, ChunYang Wang, Chao Zhan, Baishun Su

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A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images by Mishmala Sushith, A. Sathiya, V. Kalaipoonguzhali, V. Sathya

“…This paper presents a novel hybrid deep learning framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for diabetic retinopathy (DR) early detection and progression monitoring using retinal fundus images. …”
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Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks. by Seok Jin Bang, Yong-Tae Kim, Young Jae Kim, Kwang Gi Kim

“…Artificial intelligence models, U-Net, and a fully connected network (FCN), were used to train each type of image data. …”
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Multiclass aerial image recognition using improved Black Widow Optimization with deep learning on unmanned aerial networks imaging by Mahmoud Ragab, Bandar M. Alghamdi, Sami Saeed Binyamin, Sultan Algarni, Roobaea Alroobaea, Abdullah M. Baqasah, Majed Alsafyani

“…UAV-based aerial image classification improves the analysis of large-scale geographical and environmental data. …”
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Advanced deep learning models based on neutrosophic logic for the analysis of brain tumor medical images by Dina Atef, Doaa El Shaha

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CSTFNet: A CNN and Dual Swin-Transformer Fusion Network for Remote Sensing Hyperspectral Data Fusion and Classification of Coastal Areas by Dekai Li, Harold Neira-Molina, Mengxing Huang, Syam M.S., Yu Zhang, Zhang Junfeng, Uzair Aslam Bhatti, Muhammad Asif, Nadia Sarhan, Emad Mahrous Awwad

“…In recent years, the convolutional neural network (CNN) framework and transformer models have demonstrated strong performance in HSI classification, especially in applications requiring precise change detection and analysis. …”
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The Image of City as Seen through Social Media Photographs: A Case Study of the Historical Context of Tabriz by Mahsa Chizfahm Daneshmandian, Morteza Mirgholami, Abbas Ghafari, Yaser Shahbazi

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Tooth segmentation in panoramic dental radiographs using deep convolution neural network -Insights from subjective analysis by Suvarna Bhat, Gajanan K. Birajdar, Mukesh D. Patil

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Analysis and training of a traffic sign recognition neural network model by A. U. Mentsiev, T. G. Aigumov, E. M. Abdulmukminova

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Best practices for AI-based image analysis applications in aquatic sciences: The iMagine case study by Elnaz Azmi, Khadijeh Alibabaei, Valentin Kozlov, Tjerk Krijger, Gabriele Accarino, Sakina-Dorothée Ayata, Amanda Calatrava, Marco Mariano De Carlo, Wout Decrop, Donatello Elia, Sandro Luigi Fiore, Marco Francescangeli, Jean-Olivier Irisson, Rune Lagaisse, Martin Laviale, Antoine Lebeaud, Carolin Leluschko, Enoc Martínez, Germán Moltó, Igor Ruiz Atake, Antonio Augusto Sepp Neves, Damian Smyth, Jesús Soriano-González, Muhammad Arabi Tayyab, Vanessa Tosello, Álvaro López García, Dick Schaap, Gergely Sipos

“…One of the objectives of this project is to provide an AI platform that leverages AI-powered tools to improve the processing and analysis of imaging data from marine and freshwater ecosystems, contributing to the study of the health of oceans, seas, coasts, and inland waters. …”
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