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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Finding Optimal Nash Equilibria in Multiplayer Games via Correlation Plans 22

Finding Optimal Nash Equilibria in Multiplayer Games via Cor...

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22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Zhang, Youzhi An, Bo Subrahmanian, V.S. Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation CAS Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science Northwestern University Evanston United States

Designing efficient algorithms to compute a Nash Equilibrium (NE) in multiplayer games is still an open challenge. In this paper, we focus on computing an NE that optimizes a given objective function. Finding an optimal NE in multiplayer games can be formulated as a mixed-integer bilinear program by introducing auxiliary variables to represent bilinear terms, leading to a huge number of bilinear terms, making it hard to solve. To overcome this challenge, we propose a novel algorithm called CRM based on a novel mixed-integer bilinear program with correlation plans for some subsets of players, which uses Correlation plans with their Relations to strictly reduce the feasible solution space after the convex relaxation of bilinear terms while Minimizing the number of correlation plans to significantly reduce the number of bilinear terms. Experimental results show that our algorithm can be several orders of magnitude faster than the state-of-the-art baseline. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Computation theory

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The Four Most Basic Elements In Machine Cognition

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Data Intelligence 2024年第2期6卷 297-319页

作者： Deyi Li Jialun Yin Tianlei Zhang Wei Han Hong Bao Chinese Association for Artificial Intelligence Beijing 100101China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Trunk Beijing 100101China AIForceTech Beijing 100101China College of Robotics Beijing Union UniversityBeijing 100101China

The widespread use of ChatGPT has normalized the dialogue Turing *** meet this challenge,China's major national development strategy suggests that for a new generation of artificial intelligence,it is first necessary to answer the big questions raised by Turing in 1950 from the perspective of cognitive physics:Can machines think?How do machines think?How do machines cognize?Whether it is carbon-based human cognition or silicon-based machine cognition,it is an interaction between complex constructs composed of the four most basic elements:matter,energy,structure,and *** humans and machines depend on negative entropy for living,and time is the cornerstone of *** and time are parasitic on matter and energy in physical space,forming hard-structured *** soft-structured ware in cognitive space is mind,which is parasitic on the hard-structured ware or other existing soft-structured ware,and constitutes a rich hierarchy of multi-scale feelings,concepts,information,and ***"abstraction"from the symbolic school of artificial intelligence,"association"from the connectionist school,and"interaction"from the behaviorist school,the core of cognition is established on the shoulders of such scientific giants such as Schrodinger,Turing and *** and hard-structured ware *** machine can comprise heterogeneous hard-structured ware,such as field programmable gate arrays(FPGAs),data processing units(DPUs),central processing units(CPUs),graphics processing units(GPUs),tensor processing units(TPUs),and *** can also be implanted with the"Baby Cognitive Nucleus"which is hard-structured ware genetically inherited and naturally evolved to form the embodied *** the hard-structured ware is parasitized by rich,multi-scale soft-structured *** regulating matter and energy through soft-structured ware,machines produce orderly events,form coordinated and orderly thinking *** heterogeneous sensors configur

关键词： Cognitive physical science Negentropy Soft-structured ware Baby cognitive nucleus Intense thinking Embodied turing test

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Enhanced Local Pattern Transformation Based Feature Extraction for Identification of Parkinson’s Disease Using Gait Signals

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SN computer science 2023年第2期4卷 1-12页

作者： Klinton Amaladass, P. Subathra, M.S.P. Jeba Priya, S. Sivakumar, Mahima Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

One of the most common ailments, especially among the elderly, is Parkinson's disease (PD). Although previous research has demonstrated that heuristics can diagnose Parkinson's disease using decisive signs like tremor, muscular rigidity, movement disorders, and voice disorders, it has also been reported that current approaches, which rely on simple motor tasks, are limited and lack stability and accessibility. The purpose of this study is to identify a novel cost-effective and time-efficient early detection technique for the prediction of this disease using a signal processing feature extraction approach namely, Shifted Extended Local Binary Pattern (S-ELBP) using gait signals. The features extracted using the proposed methods are given as the input to an artificial neural network (ANN) to classify them as Healthy or Parkinson’s. The proposed method has quite promising results when evaluated using different performance metrics. The method has yielded accuracy: 97.6%, specificity: 95.71%, sensitivity: 99, positive predictive value (PPV): 97.2%, negative predictive value (NPV): 98.8%, Matthews correlation coefficient (MCC): 95.4%, F1-score: 97.9%, and geometric mean: 97.19%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction Parkinson’s disease Parkinson’s gait Shifted extended local binary pattern

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Medical Recommendation System Based on Collaborative Filtering Algorithms

Medical Recommendation System Based on Collaborative Filteri...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Diana Bogdanova Gyuzel Shakhmametova Albert Niiazgulov Department of Computer Science and Robotics Ufa University of Science and Technology Ufa Russia

The article deals with the issues of designing a medical recommendation system based on the collaborative filtering algorithm. The problem under consideration is a large number of medical errors, which are one of the most common causes of patient deaths. In Russia alone, more than 70 thousand people suffer severe complications every year for this reason. The aim of the research is to analyze the possibility of designing and implementing software that reduces the probability of occurrence of medical errors and allows to realize the automation of prescribing by a doctor of the optimal treatment for a particular patient. The article analyzes the subject area and problem situation, identifies the causes of a large number of medical errors in public medical institutions in Russia. The problem statement is considered, mathematical support of the recommendation system based on the algorithm of collaborative filtering is proposed. The analysis of methods for solving the problem is given. The implementation of the recommendation system in the Python programming language is considered, the results of research in the form of a computational experiment are analyzed and interpreted. According to the results of the research, it is concluded that the proposed implementation of the medical recommendation system based on the collaborative filtering algorithm can be used in the further design of a full-fledged system and then used in medical institutions as a part of the system.

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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Uncertainty-aware Condensation for OCT Cervical Dataset Combining Coreset Selection and Distillation

Uncertainty-aware Condensation for OCT Cervical Dataset Comb...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xiong, Yuxuan Xu, Yongchao Zhang, Yan Gui, Kai Du, Bo School of Computer Science Wuhan University Wuhan China Renmin Hospital Wuhan University Department of Obstetrics and Gynecology Wuhan China Yijiahe Robotics Research Institute Nanjing China

ISBN: (纸本)9798350386226

Optical Coherence Tomography (OCT) cervical images offer a sensitive and non-invasive solution for screening cervical cancer. Currently, utilizing deep learning techniques to interpret these images has emerged as a promising approach for computer-aided diagnosis. However, challenges in computational efficiency and privacy protection persist in this field. To address these issues, we propose an uncertainty-aware filtering before distillation (UFD) framework to compress the OCT cervical dataset in a trustworthy and efficient manner. Our approach involves pre-training an uncertainty proxy to evaluate the importance of each sample, allowing us to extract a valuable subset (i.e., coreset) from the original dataset. We then apply importance-weighted distribution matching for dataset distillation. The distribution matching loss is calculated as the distance between the class centers of the uncertainty-weighted coreset and the synthetic distilled dataset. Extensive experiments validate the feasibility and effectiveness of our proposed UFD framework. It is worth noting that after selecting the most representative samples from the original OCT dataset through coreset selection, the performance of the trained network is comparable to, or sometimes even slightly higher than, that of the network trained with all the original data. This indicates that automated dataset compression and cleaning techniques are crucial for improving image classification problems in medical imaging datasets, such as OCT and ultrasound, which are rich in noise and erroneous samples. © 2024 IEEE.

关键词： Optical coherence tomography

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Metrics to Estimate Model Comprehension: Towards a Reliable Quantification Framework 19

Metrics to Estimate Model Comprehension: Towards a Reliable ...

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19th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2024

作者： Tenbergen, Bastian Daun, Marian Department of Computer Science State University of New York at Oswego Oswego United States Center of Robotics Technical University of Applied Sciences Würzburg-Schweinfurt Schweinfurt Germany

ISBN: (纸本)9789897586965

Model-driven development has established itself as one of the core practices in software engineering. Increases in quality demands paired with shorter times to market and increased mission-criticality of software systems have sensitized software engineering practitioners to make use of not only formal, but also semi-formal models, particularly graphical diagrams to express the system under development in ways that facilitate collaboration, validation & verification, as well as configuration and runtime monitoring. However, what does and does not constitute a "good" model, i.e., a model that is fit for a practical purpose? While some model quality frameworks exist, the trouble with most of these is that they often lack the ability to concretely quantify and thereby objectively differentiate a "good" from a "poor" model, i.e., models that can be easily understood by the model reader. Without being able to reliably produce easily comprehensible models, training new team members during on-boarding or educating software engineering students is dramatically hindered. In this paper, we report on a research trajectory towards reliably measuring the comprehensibility of graphical diagrams. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Verification

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Scanning the Issue – Sept 2024 Additional Papers

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 10457-10461页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

Summary form only: Abstracts of articles that will be presented in future issues of this publication.

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