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Glaucoma identification with retinal fundus images using deep learning: systematic review

Informatics in Medicine Unlocked 2025年 56卷

作者： Dulani Meedeniya Thisara Shyamalee Gilbert Lim Pratheepan Yogarajah Department of Computer Science and Engineering University of Moratuwa Moratuwa 10400 Sri Lanka First-Year Division Faculty of Computing Sri Lanka Institute of Information Technology Malabe 10115 Sri Lanka SingHealth AI Health Program SingHealth Group Singapore 168582 Singapore School of Computing Engineering and Intelligent System Ulster University Londonderry BT48 7JL United Kingdom

Glaucoma is a leading cause of blindness, affecting millions of people worldwide. It is a chronic eye condition that damages the optic nerve and, if left untreated, can lead to vision loss and a decreased quality of life. Therefore, there is a need to explore practical and reliable mechanisms for glaucoma identification. This study systematically reviews deep-learning approaches for glaucoma identification using retinal fundus images from 2018 to 2024. Compared to existing survey studies, we cover the latest research, including several public retinal fundus image datasets, and focus on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection.

关键词： Artificial intelligence Classification Deep learning Explainability Glaucoma Segmentation Trustworthiness

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Angle and polarization independent plasmonic infrared band-stop filter with metallic disk on SiO2

Angle and polarization independent plasmonic infrared band-s...

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16th Opto-Electronics and Communications Conference, OECC 2011

作者： Abbas, Mohammed Nadhim Cheng, Cheng-Wen Chang, Yia-Chung Shih, Min-Hsiung Research Center for Applied Sciences Academia Sinica Taipei 11529 Taiwan Institute of Physics and Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Engineering and System Science NTHU Hsinchu 300 Taiwan

ISBN: (纸本)9781612842882

We demonstrated Ag/SiO2 metallic disk structure can be applied as an efficient narrow stop band filter in the IR region. The absorption spectra of such structure are investigated both theoretically and experimentally. The metallic disk exhibits only one significant localized surface plasmon polariton mode for both TM and TE polarizations simultaneously which can give an ultrahigh immunity to incident angles and polarization direction. © 2011 National Sun Yat-Sen Univ.

关键词： Silica

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Machine learning of organic solvents reveals an extraordinary axis in Hansen space as indicator of spherical precipitation of polymers

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Aggregate 2023年第5期4卷 207-213页

作者： Yuta Ihara Hiroshi Yamagishi Masanobu Naito Yohei Yamamoto Department of Materials Science Faculty of Pure and Applied Sciencesand Tsukuba Research Center for Energy Materials Science(TREMS)University of TsukubaTsukubaJapan Research and Services Division of Materials Data and Integrated System(MaDIS) National Institute for Materials Science(NIMS)TsukubaJapan Program in Materials Science and Engineering Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukubaJapan

Machine learning is an emerging tool in the field of materials chemistry for uncovering a principle from large ***,we focus on the spherical precipitation behavior of polymers and computationally extract a hidden trend that is orthogonal to the availability bias in the chemical *** constructing a dataset,four polymers were precipitated from 416 solvent/nonsolvent combinations,and the morphology of the resulting precipitates were *** dataset was subjected to computational investigations consisting of principal component analysis and machine learning based on random forest model and support vector ***,we eliminated the effect of the availability bias and found a linear combination of Hansen parameters to be the most suitable variable for predicting precipitation *** predicted appropriate solvents are those with low hydrogen bonding capability,low polarity,and small molecular ***,we found that the capability for spherical precipitation is orthogonal to the availability bias and forms an extraordinary axis in Hansen space,which is the origin of the conventional difficulty in identifying the *** extraordinary axis points toward a void region,indicating the potential value of synthesizing novel solvents located therein.

关键词： chemical space computational chemistry machine learning polymers precipitation

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An Expert Perspective of Errors in Endoscope Reprocessing

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2011年第1期55卷 748 - 752页

作者： Emily A. Hildebrand Russell J. Branaghan Brooke L. Neuman Jonathan Jolly T.B. Garland Mistey Taggart Dana R. Epstein Judith Babcock-Parziale Victoria Brown Phoenix VA Health Care System Cognitive Science and Engineering Program Arizona State University Phoenix VA Health Care SystemCognitive Science and Engineering Program Arizona State University Southern Arizona VA Health Care System

Endoscope reprocessing has been identified as a complex procedure that, when completed incorrectly, can lead to patient outbreaks due to cross contamination. Most reported incidents cite human error as the cause, but little research to date has explored the human factors related issues surrounding reprocessing procedures. The purpose of this study was to interview experts in endoscope reprocessing to determine the most difficult steps and error prone tasks in the procedure as well as identify potential contributing factors to error. Relationships between tasks and contributing factors are examined and implications for future research are discussed.

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Contamination-free cell lysis microfluidic device using cell cross over

Contamination-free cell lysis microfluidic device using cell...

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13th International Conference on Miniaturized systems for Chemistry and Life sciences, MicroTAS 2009

作者： Mun, Byung Phil Jung, Seung Mo Yoon, Sang Youl Lee, Jong Hyun Yang, Sung School of Information and Mechatronics Gwangju Institute of Science and Technology Korea Republic of Graduate Program of Medical System Engineering Gwangju Institute of Science and Technology Korea Republic of Department of Nanobio Materials and Electronics Gwangju Institute of Science and Technology Korea Republic of

ISBN: (纸本)9780979806421

Complicated and time-consuming cell purification processes prevent a micro cell lysis device from integrating with LOC (Lab-on-a-chip) or μ-TAS (μ-Total Analysis system) devices for cell analysis. In this paper, we present a microfluidic cell lysis device using a contamination-free cell cross over method in order to minimize the cell pretreatment procedure. © 2009 CBMS.

关键词： Microfluidics

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Overall Equipment Effectiveness: Required but not Enough—An Analysis Integrating Overall Equipment Effect and Data Envelopment Analysis

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Global Journal of Flexible systems Management 2020年第2期21卷 191-206页

作者： Piran, Fabio Antonio Sartori De Paris, Alaércio Lacerda, Daniel Pacheco Camargo, Luis Felipe Riehs Serrano, Rosiane Cassel, Ricardo Augusto Production and System Engineering Graduate Program Universidade Do Vale Do Rio Dos Sinos São Leopoldo RS Brazil Federal Institute of Education Science and Technology of Rio Grande Do Sul- Campus Erechim Erechim RS Brazil Production Engineering Graduate Program Universidade Federal Do Rio Grande Do Sul Porto Alegre RS Brazil

The efficiency levels at which companies turn inputs into outputs may increase their competitive capacity. Nevertheless, in the literature, there is a lack of methods allowing evaluation of the performance of operations in production systems that take into account all of their components. The objective of this study is to present an analysis of efficiency for a production system using DEA and OEE in an integrated manner (DEA/OEE). To achieve this objective, we conducted a case study of a bus manufacturer and a comparative analysis using the results obtained by DEA/OEE compared with the results of the OEE measured in an operation of the company studied. The efficiency measured by DEA/OEE presents differences in relation to OEE results. The analysis of the causes of such differences pointed out that action taken to improve the OEE indicator decreased the efficiency of the operation analyzed. This study evidences that decision-making focused on improving OEE on its own may lead to an increase in the consumption of resources during an operation. Such an increase does not necessarily increase production levels and may reduce technical efficiency. © 2020, Global Institute of Flexible systems Management.

关键词： Data envelopment analysis Operational performance evaluation Overall equipment effectiveness Performance measurement

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Quantification of the explosion risk of biogas-hydrogen-air mixtures with various hydrogen fractions in a 20 L chamber

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Process Safety and Environmental Protection 2025年 199卷

作者： Joonsik Kim Akihiro Ueda Wookyung Kim Chankyu Kang Yangkyun Kim Hydrogen-infra. Research Center(HiRC) Korea Institute of Civil Engineering and Building Technology(KICT) 64 Mado-ro 182beon-gil Mado-myeon Hwaseong-si Gyeonggi-do 10223 Republic of Korea Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan School of Social Safety System Engineering Research Center for Safety and Health Hankyong National University Anseong 17579 South Korea

The explosion risk and severity of biogas-hydrogen-air mixtures are investigated by measuring the explosion parameters—namely, the maximum explosion pressure, maximum pressure rise rate, deflagration index, and laminar burning velocity—using a 20 L spherical explosion chamber. Accurate deflagration indices are calculated using the maximum pressure rise rate derived from a smoothed pressure–time curve. The values of all parameters increase monotonously with increasing hydrogen fraction in biogas-hydrogen-air mixtures: the maximum explosion pressure increases by up to 1.33 times, the maximum pressure rise rate and deflagration index increase by up to 2.54 times, and the laminar burning velocity increases by up to 1.7 times. Hydrogen addition shortens the deflagration duration and accelerates pressure build-up. The addition of a small amount of hydrogen leads to an explosion risk close to that of pure methane. In summary, the addition of hydrogen into biogas-air mixtures significantly increases the explosion risk and severity.

关键词： Explosion risk Risk assessment Deflagration index Bio-hydrogen Hydrogen production

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