The SmARTR pipeline: A modular workflow for the cinematic rendering of 3D scientific imaging data by Simone Macrì, Nicolas Di-Poï

“…This versatile pipeline supports multiscale visualizations—from tissue-level to whole-organism details across diverse living organisms—and is adaptable to various imaging sources. Its modular design and customizable rendering scenarios, enabled by the global illumination modeling and programming modules available in the free MeVisLab software and seamlessly integrated into detailed SmARTR networks, make it a powerful tool for 3D data analysis. …”
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Application of improved and efficient image repair algorithm in rock damage experimental research by Mingzhe Xu, Xianyin Qi, Diandong Geng

“…The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. …”
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Combining Deep Learning and Street View Images for Urban Building Color Research by Wenjing Li, Qian Ma, Zhiyong Lin

“…The framework is composed of two phases: “deep learning” and “quantitative analysis.” In the “deep learning” phase, a deep convolutional neural network (DCNN)-based color extraction model is designed to automatically learn building color information from street view images; in the “quantitative analysis” phase, building color is quantitatively analyzed at the overall and local levels, and a color clustering model is designed to quantitatively display the color relationship to comprehensively understand the current status of urban building color. …”
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A Novel Approach to Retinal Blood Vessel Segmentation Using Bi-LSTM-Based Networks by Pere Marti-Puig, Kevin Mamaqi Kapllani, Bartomeu Ayala-Márquez

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A multi-domain dual-stream network for hyperspectral unmixing by Jiwei Hu, Tianhao Wang, Qiwen Jin, Chengli Peng, Quan Liu

“…This approach allows us to uncover pure endmember data that is hidden within the original hyperspectral image (HSI). …”
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A Dual-Branch Network of Strip Convolution and Swin Transformer for Multimodal Remote Sensing Image Registration by Kunpeng Mu, Wenqing Wang, Han Liu, Lili Liang, Shuang Zhang

“…Multimodal remote sensing image registration aims to achieve effective fusion and analysis of information by accurately aligning image data obtained by different sensors, thereby improving the accuracy and application value of remote sensing data in engineering. …”
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Saccades influence functional modularity in the human cortical vision network by George Tomou, Bianca R. Baltaretu, Amirhossein Ghaderi, J. Douglas Crawford

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Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement by Wang Rongcai, Yan Hao, Dong Enzhi, Cheng Zhonghua, Li Yuan, Jia Xisheng

“…Convolutional neural network (CNN) has garnered significant attention owing to the exceptional capability in extracting image features. …”
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Multi class aerial image classification in UAV networks employing Snake Optimization Algorithm with Deep Learning by Alanoud Al Mazroa, Nuha Alruwais, Muhammad Kashif Saeed, Kamal M. Othman, Randa Allafi, Ahmed S. Salama

“…Abstract In Unmanned Aerial Vehicle (UAV) networks, multi-class aerial image classification (AIC) is crucial in various applications, from environmental monitoring to infrastructure inspection. …”
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Renal Biopsy Pathological Tissue Segmentation: A Comprehensive Review and Experimental Analysis by Yan Liu, Taiping Wang, Xinxin He, Wei Wang, Qiang He, Juan Jin

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Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. …”
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Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis by R. Nandhini Abirami, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Usman Tariq, Chuan-Yu Chang

“…The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. …”
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Impact of eye fundus image preprocessing on key objects segmentation for glaucoma identification by Sandra Virbukaitė, Jolita Bernatavičienė

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Enhancing Recognition and Categorization of Skin Lesions with Tailored Deep Convolutional Networks and Robust Data Augmentation Techniques by Syed Ibrar Hussain, Elena Toscano

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ONLINE FUZZY CLUSTERING OF HIGH DIMENSION DATA STREAMS BASED ON NEURAL NETWORK ENSEMBLES by Yevgeniy Bodyanskiy, Iryna Perova, Polina Zhernova

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Deep learning-driven medical image analysis for computational material science applications by Li Lu, Mingpei Liang

“…IntroductionDeep learning has significantly advanced medical image analysis, enabling precise feature extraction and pattern recognition. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

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A Dynamic Hill Cipher with Arnold Scrambling Technique for Medical Images Encryption by Yuzhou Xi, Yu Ning, Jie Jin, Fei Yu

“…Unlike the THC, the proposed DHCAST uses a time-varying matrix as its secret key, which greatly increases the security of the THC, and the new DHCAST is successfully applied in medical images encryption. In addition, the new DHCAST method employs the Zeroing Neural Network (ZNN) in its decryption to find the time-varying inversion key matrix (TVIKM). …”
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ChromosomeNet: Deep Learning-Based Automated Chromosome Detection in Metaphase Cell Images by Chih-En Kuo, Jun-Zhou Li, Jenn-Jhy Tseng, Feng-Chu Lo, Ming-Jer Chen, Chien-Hsing Lu

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OpenFungi: A Machine Learning Dataset for Fungal Image Recognition Tasks by Anca Cighir, Roland Bolboacă, Teri Lenard

“…The quality of the dataset is demonstrated by solving a classification problem with a simple convolutional neural network. A thorough experimental analysis was conducted, where six performance metrics were measured in three distinct validation scenarios. …”
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