检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Yan, Xu Fan, Xiaoliang Yang, Peizhen Wu, Zonghan Pan, Shirui Chen, Longbiao Zang, Yu Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Computer Science & Technology Department Xiamen University Xiamen China Centre for Artificial Intelligence FEIT University of Technology Sydney Australia Department of Data Science and AI Faculty of Information Technology Monash University Australia

—Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existing dynamic embedding methods on TIG discretely update node embeddings merely when an interaction occurs. They fail to capture the continuous dynamic evolution of embedding trajectories of nodes. In this paper, we propose a two-module framework named ConTIG, a continuous representation method that captures the continuous dynamic evolution of node embedding trajectories. With two essential modules, our model exploit three-fold factors in dynamic networks which include latest interaction, neighbor features and inherent characteristics. In the first update module, we employ a continuous inference block to learn the nodes’ state trajectories by learning from time-adjacent interaction patterns between node pairs using ordinary differential equations. In the second transform module, we introduce a self-attention mechanism to predict future node embeddings by aggregating historical temporal interaction information. Experiments results demonstrate the superiority of ConTIG on temporal link prediction, temporal node recommendation and dynamic node classification tasks compared with a range of state-of-the-art baselines, especially for long-interval interactions prediction. © 2021, CC BY.

关键词： Ordinary differential equations

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Exploring disparities of teleconsultation readiness: A comparative analysis of healthcare facilities in Indonesia

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DIGITAL HEALTH 2024年 10卷

作者： Daniel Chriswinanto Adityo Nugroho Katherina Adisaputro Mitra Andini Sigilipoe Ida Ayu Triastuti Suryani Hutomo Ardhinta Bellano Septarda Aryososepti Yuwono Caraka Randi Yusuf Dedik Sulistiawan Balqis Gusbela Jason C Hsu Emily Chia-Yu Su Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta IndonesiaGraduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta Indonesia Faculty of Public Health Universitas Ahmad Dahlan Yogyakarta IndonesiaSchool of Public Health Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University TaipeiClinical Data Center Office of Data Science Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiResearch Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiInstitute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei

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CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization 16th

CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Acti...

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Yuxi Lin, Weiyao See, John Xu, Ning Xu, Shugong Yan, Ke Yang, Cong Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Institute for Advanced Communication and Data Science Shanghai University Shanghai China Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Adobe Research San Francisco United States Clobotics Shanghai China

ISBN: (纸本)9783030585167

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD), an original end-to-end trainable framework for efficient spatio-temporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatio-temporal action tubes from video streams, and then refines the tubes location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our framework. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, the proposed CFAD achieves competitive results on action detection benchmarks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3 faster than the nearest competitor. © 2020, Springer Nature Switzerland AG.

关键词： Tubes (components)

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Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications – an ISPOR Working Group Report

arXiv

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arXiv 2024年

作者： Fleurence, Rachael L. Wang, Xiaoyan Bian, Jiang Higashi, Mitchell K. Ayer, Turgay Xu, Hua Dawoud, Dalia Chhatwal, Jagpreet Office of the Director National Institutes of Health National Institute of Biomedical Imaging and Bioengineering BethesdaMD United States Tulane University School of Public Health and Tropical Medicine New OrleansLA United States Intelligent Medical Objects RosemontIL United States College of Medicine University of Florida FL United States Biomedical Informatics Clinical and Translational Science Institute University of Florida FL United States Office of Data Science and Research Implementation University of Florida Health GainesvilleFL United States ISPOR The Professional Society for Health Economics and Outcomes Research LawrencevilleNJ United States Center for Health & Humanitarian Systems Georgia Institute of Technology AtlantaGA United States Value Analytics Labs BostonMA United States Institute Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States National Institute for Health and Care Excellence London United Kingdom Cairo University Faculty of Pharmacy Cairo Egypt Institute for Technology Assessment Massachusetts General Hospital Harvard Medical School BostonMA United States Center for Health Decision Science Harvard University BostonMA United States

Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applications, including systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches such as prompt engineering (zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and the use of domain-specific models are introduced to improve AI accuracy and reliability. Results: Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration. The article discusses strategies to improve the accuracy of these AI tools. Conclusion: Generative AI could transform HEOR by increasing efficiency and accuracy across various applications. However, its full potential can only be realized by building HEOR expertise and addressing the limitations of current AI technologies. As AI evolves, ongoing research and innovation will shape its future role in the field. © 2024, CC BY.

关键词： Generative adversarial networks

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Observation of multi-ten TeV to sub-PeV gamma rays from the HESS J1843-033 region with the Tibet air shower array 27

Observation of multi-ten TeV to sub-PeV gamma rays from the ...

引用

27th European Cosmic Ray Symposium, ECRS 2022

作者： Amenomori, M. Asano, S. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gomi, A. Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Kurashige, D. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Liu, X.L. Lou, Y.-Q. Lu, H. Meng, X.R. Meng, Y. Munakata, K. Nagaya, K. Nakamura, Y. Nakazawa, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, L. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakakibara, Y. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Wang, H. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, N.P. Yuan, A.F. Zhai, L.M. Zhang, C.P. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zou, Y.H. Department of Physics Hirosaki University Hirosaki036-8561 Japan Department of Physics Shinshu University Matsumoto390-8621 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Tsinghua University Beijing100084 China Joint Research Center for Astrophysics Tsinghua University Beijing100084 China Department of Astronomy Tsinghua University Beijing100084 China College of Industrial Technology Nihon University Narashino275-8575 Japan National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo18

The sky region around HESS J1843-033, a southern TeV gamma-ray source, is observed by the Tibet air shower array equipped with an underground muon detector array. The analysis makes use of the data taken from February 2014 to May 2017. A gamma-ray source TASG J1844-038 is detected above 25 TeV with a 6.2 σ level, and its position is statistically consistent with that of nearby gamma-ray sources including HESS J1843-033. The energy spectrum is measured for the first time between 25 and 130 TeV and is described with dN/dE = (9.70 ± 1.89) × 10−16(E/40 TeV)−3.26±0.30 TeV−1 cm−2 s−1. The spectrum smoothly connects to that of HESS J1843-033 in the lower energy range and is consistent with the flux of LHAASO J1843-0338 at 100 TeV, and the fit to the combined spectra of the TASG, H.E.S.S., and LHAASO sources implies the cutoff at 49.5 ± 9.0 TeV in the gamma-ray spectrum. The association of TASG J1844-038 with a nearby supernova remnant G28.6-0.1 and a gamma-ray pulsar J1844-0346 is discussed. © Copyright owned by the author(s).

关键词： Gamma rays

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AskBeacon – Performing genomic data exchange and analytics with natural language

arXiv

引用

arXiv 2024年

作者： Wickramarachchi, Anuradha Tonni, Shakila Majumdar, Sonali Karimi, Sarvnaz Kõks, Sulev Hosking, Brendan Rambla, Jordi Twine, Natalie A. Jain, Yatish Bauer, Denis C. Australian e-Health Research Centre Commonwealth Scientific and Industrial Research Organisation Adelaide Australia Data61 Commonwealth Scientific and Industrial Research Organisation Sydney Australia Centre for Molecular Medicine and Innovative Therapeutics Murdoch University PerthWA6150 Australia Perron Institute for Neurological and Translational Science PerthWA6009 Australia The Barcelona Institute of Science and Technology Barcelona Spain Department of Medicine and Life Sciences Universitat Pompeu Fabra PRBB Barcelona Spain Australian e-Health Research Centre Commonwealth Scientific and Industrial Research Organisation Sydney Australia Macquarie University Applied BioSciences Faculty of Science and Engineering Macquarie Park Australia University of Sydney School - School of Medical Sciences Department of Biomedical Informatics and Digital Health Sydney Australia

Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables Large Language Models to be applied to securely shared cohorts via the GA4GH Beacon protocol. By simply "asking" Beacon, actionable insights can be gained, analyzed and made publication-ready. © 2024, CC BY-NC-SA.

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TIBET ASgamma: Dawn of sub-PeV gamma-ray astronomy 11

TIBET ASgamma: Dawn of sub-PeV gamma-ray astronomy

引用

11th Neutrino Oscillation Workshop, NOW 2022

作者： Sako, T.K. Amenomori, M. Asano, S. Bao, Y.W. Bi, X.J. Chen, D. Chen, T.L. Chen, W.Y. Chen, Xu Chen, Y. Cirennima Cui, S.W. Danzengluobu Ding, L.K. Fang, J.H. Fang, K. Feng, C.F. Feng, Zhaoyang Feng, Z.Y. Gao, Qi Gomi, A. Gou, Q.B. Guo, Y.Q. Guo, Y.Y. Hayashi, Y. He, H.H. He, Z.T. Hibino, K. Hotta, N. Hu, Haibing Hu, H.B. Hu, K.Y. Huang, J. Jia, H.Y. Jiang, L. Jiang, P. Jin, H.B. Kasahara, K. Katayose, Y. Kato, C. Kato, S. Kawahara, I. Kawashima, T. Kawata, K. Kozai, M. Kurashige, D. Labaciren Le, G.M. Li, A.F. Li, H.J. Li, W.J. Li, Y. Lin, Y.H. Liu, B. Liu, C. Liu, J.S. Liu, L.Y. Liu, M.Y. Liu, W. Liu, X.L. Lou, Y.-Q. Lu, H. Meng, X.R. Meng, Y. Munakata, K. Nagaya, K. Nakamura, Y. Nakazawa, Y. Nanjo, H. Ning, C.C. Nishizawa, M. Noguchi, R. Ohnishi, M. Okukawa, S. Ozawa, S. Qian, L. Qian, X. Qian, X.L. Qu, X.B. Saito, T. Sakakibara, Y. Sakata, M. Sako, T. Sako, T.K. Sasaki, T. Shao, J. Shibata, M. Shiomi, A. Sugimoto, H. Takano, W. Takita, M. Tan, Y.H. Tateyama, N. Torii, S. Tsuchiya, H. Udo, S. Wang, H. Wang, Y.P. Wangdui Wu, H.R. Wu, Q. Xu, J.L. Xue, L. Yang, Z. Yao, Y.Q. Yin, J. Yokoe, Y. Yu, N.P. Yuan, A.F. Zhai, L.M. Zhang, C.P. Zhang, H.M. Zhang, J.L. Zhang, X. Zhang, X.Y. Zhang, Y. Zhang, Yi Zhang, Ying Zhao, S.P. Zhaxisangzhu Zhou, X.X. Zhou, Y.H. Department of Physics Hirosaki University Hirosaki036-8561 Japan Department of Physics Shinshu University Matsumoto390-8621 Japan School of Astronomy and Space Science Nanjing University Nanjing210093 China Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China National Astronomical Key Laboratory of Observatories Chinese Academy of Sciences Beijing100012 China Department of Mathematics and Physics Tibet University Lhasa850000 China Department of Physics Hebei Normal University Shijiazhuang050016 China University of Chinese Academy of Sciences Beijing100049 China Shandong University Qingdao266237 China Institute of Modern Physics SouthWest Jiaotong University Chengdu610031 China Faculty of Engineering Yokohama National University Yokohama240-8501 Japan Faculty of Engineering Kanagawa University Yokohama221-8686 Japan Faculty of Education Utsunomiya University Utsunomiya321-8505 Japan Faculty of Systems Engineering Shibaura Institute of Technology Omiya330-8570 Japan Institute for Cosmic Ray Research University of Tokyo Kashiwa277-8582 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa190-0014 Japan National Center for Space Weather China Meteorological Administration Beijing100081 China School of Information Science and Engineering Shandong Agriculture University Taian271018 China Department of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China Tsinghua University Beijing100084 China Joint Research Center for Astrophysics Tsinghua University Beijing100084 China Department of Astronomy Tsinghua University Beijing100084 China College of Industrial Technology Nihon University Narashino275-8576 Japan National Institute of Informatics Tokyo101-8430 Japan National Institute of Information and Communications Technology Tokyo18

The Tibet air shower (AS) array and underground water-Cherenkov-type muon detector (MD) array have been successfully operating since 2014 at an altitude of 4, 300 m in Tibet, China. This hybrid experiment is useful not only for the gamma-ray observation but also for the energy spectrum measurement in the knee energy region of the cosmic-ray energy spectrum. The energy and arrival directions of air showers are determined by the AS array, while the number of muons contained in air showers is counted with the MD array. In terms of gamma-ray observation, the MD array enables us to suppress more than 99.9% of background cosmic rays against gamma-ray signals above 100 TeV. Our gamma-ray and cosmic-ray data from the AS+MD array is well reproduced by our detailed air shower simulations. Here we report on the latest results from the Tibet ASγ experiment, especially focusing on the world’s first detection of sub-PeV gamma rays from the Crab Nebula and the world’s first detection of sub-PeV diffuse gamma-ray emissions from the Galactic plane. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Cosmic rays

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Social network analysis to detect influential actors with Indonesian hastags using the centrality method

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AIP Conference Proceedings 2023年第1期2680卷

作者： Edi Surya Negara Keni Keni Ria Andryani Rezki Syaputra Syaputra Yeni Widyanti Data Science Interdisciplinary Research Center Informatics Department Faculty of Computer Science Universitas Bina Darma Indonesia Faculty Economics & Business Universitas Tarumanagara Jl. Letjen S.Parman No.1 Jakarta 11440 Indonesia Information System Department Faculty of Computer Science Universitas Bina Darma Indonesia Faculty Economics & Business Universitas Bina Darma Indonesia

data growth is currently happening rapidly globally, especially data that comes from social media. Social media Twitter is one of the social media commonly used in Indonesia, with data on Twitter that can be used as a means of information or news and can be analyzed using a graph database. This study utilizes a graphic database sourced from social media twitter. This study uses the Social Network Analysis (SNA) method to analyze graphs that represent social networks between users to find out which actors are the most influential in spreading news or information. Graph database and visualization software used in this study is Gephi 0.9.2. In determining the actors who have the most influence on the hashtag #Indonesia, it is calculated using Centrality which consists of degree Centrality, Betweenness Centrality, Closenness Centrality. The sample used in this study is a tweet that mentions, retweetes, or replies to account interactions using #Indonesia. The result is that there are 1064 nodes (accounts) and there are 1261 edge activities involved in the interactions that the actor found or the most influential account on the #Indonesia network in total is an account called bob_bay.

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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