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10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Ouyang, Shuyi Xu, Yingying Wang, Weibin Li, Yinhao Wang, Fang Chen, Qingqing Lin, Lanfen Chen, Yen-Wei Hu, Hongjie College of Computer Science and Technology Zhejiang University Hangzhou310000 China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China College of Information Science and Engineering Ritsumeikan University Shiga Kusatsu525-8577 Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University Hangzhou310000 China

ISBN: (纸本)9789811934391

Early recurrence (ER) after surgery is one of the main causes of death in Hepatocellular Carcinoma (HCC). Making an effective prediction of the risk of ER is important in developing treatment strategies. The existing methods generally use radiomic features or simple deep learning features, which have insufficient feature representation capabilities. Here, we present a deep learning-based strategy for obtaining enriched multi-phase fusion features that can effectively represent feature information. We present an effective preoperative prediction model that uses multi-phase CT scans as input. Our proposed framework significantly utilizes and enhances multi-phase contextual information by effectively performing multi-phase feature fusion. We also present a novel backbone network with attention mechanisms called Inv-ResNet, that can extract more discriminative features from each CT phase using ResNet block and involution operator. Further, we design a Multi-Phase Feature Fusion Module that allows for global-Squeeze and phase-Excitation of important feature weights across all CT phases to selectively fuse features. In comparison to existing methods of preoperative prediction of ER of HCC, our method achieves 72.70% accuracy and a value of 0.7478 area under the ROC curve (AUC), which is the highest accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computerized tomography

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applications of Machine Learning in Medicine: Current Trends and Prospects

Applications of Machine Learning in Medicine: Current Trends...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Muhammad Aamir Sibghatullah Bazai Uzair Aslam Bhatti Zaheer Ahmed Dayo Jing Liu Kun Zhang College of Computer Science Huanggang Normal Univeristy Huanggang China Department of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences Quetta Pakistan School of Information and Communication Engineering Hainan University Haikou China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China

With the continuous development of information technology and medical data information, more and more clinicians recognize artificial intelligence or will completely change medical practice by using advanced machine learning methods. The potential of using machine learning and predictive analysis to customize specific treatments for individuals is currently under research. Machine learning can learn a large number of medical data and explore the dependencies in data concentration, forming a corresponding medical model that can quickly and accurately predict new data, which is conducive to the early diagnosis of diseases and assisted clinical decisions. Clinical medicine faces the status quo of the relative shortage of medical resources and the identification and rapid diagnosis and treatment of critically ill emergency patients. In the era of big data, clinical demand is driven by clinical needs, and intelligent medical care provided by machines is the key to tackling the aforementioned issues.

关键词： Industries Wireless communication Sociology Machine learning Medical services Big data Predictive models

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia computer science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius Laith Abualigah Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy Moth-Flame algorithm evaluation

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions

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Procedia computer science 2024年 235卷 3438-3446页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays.

关键词： Covid19 X-ray Vision-transformer Patch-size Classification

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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

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Procedia computer science 2024年 235卷 1824-1831页

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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MATNet: MRI Super-Resolution with Multiple Attention Mechanisms

MATNet: MRI Super-Resolution with Multiple Attention Mechani...

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New Trends in Computational Intelligence (NTCI), International Conference on

作者： Longfeng Shen Fenglan Qin Yingjie Zhang Qiong Wang Bowen Hu Wei Zhao Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) Hefei Comprehensive National Science Center Institute of Artificial Integence Hefei China School of Computer Science and Technology Huaibei Normal University Huaibei Anhui China Anhui Big-Data Research Center on University Management Huaibei Anhui China People's Hospital of Huaibei City Anhui China

High-resolution magnetic resonance imaging (MRI) provides a clear anatomical structure for diagnosis, however, its high cost makes it unsuitable in practice. On the contrary, low-resolution MRI cannot provide fine structural information but is resource and time efficient. Super-resolution methods are high-resolution images obtained from low-resolution MRI. However, existing MRI super-resolution methods suffer from the following defects: (1) CNN-based super-resolution methods lack the ability to build relationships for remote features. (2) While models using transformer based on self-attentive mechanism have achieved a breakthrough in super-resolution, the single self-attentive model fails to fully utilize the useful features of the input image. Thus, in this paper, an MRI super-resolution network (MATNet) based on a multi-attention mechanism is proposed that can utilize multiple useful image features from different dimensions for image super-resolution, improved super-resolution performance. To enable the network to obtain noise-free image features before sampling the image, we also added a denoising module to it. Finally, we prove the effectiveness of the model using the IXI public dataset.

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Modernizing Home Protection: An IoT-Driven Approach with Smart Lock and Android application

Modernizing Home Protection: An IoT-Driven Approach with Sma...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Summaiya Rajput Noureen Talpur Rozan Boudville Ghulam Mustafa E Abro Beenish Talpur Faizan Zahid Department of Telecommunications Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Computer Information Science Department Centre for Research in Data Science (CeRDaS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Electrical Engineering Studies College of Electrical Engineering Universiti Teknologi MARA Permatang Pauh Pulau Pinang Malaysia Interdisciplinary Research Center for Aviation and Space Exploration King Fahd University of Petroleum and Minerals Dhahran Dammam Kingdom of Saudi Arabia Department of Electrical Electronics and Computer Engineering University of Catania Italy

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

In the field of access control locks, conventional methods often require users to download applications with Bluetooth connectivity, and biometric measures such as fingerprints or face recognition that introduce challenges like access to only authorized parties, limited device accessibility and intricate registration processes. To address these challenges, the research paper presents an innovative IoT-based Smart Door Lock System, aiming to redefine access control. Diverging from traditional approaches, this system eliminates the need for application downloads and simplifies the complexities tied to biometrics. The core innovation lies in the simplicity of a user-friendly 4-digit PIN for primary access, prioritizing ease of use. To overcome real-time access constraints, the system introduces One-time Password (OTP) and One Touch Access (OTA) modes. These modes eliminate the intricacies linked with traditional methods, ensuring immediate and hassle-free access for both authorized and unauthorized users.

关键词： Technological innovation databases Face recognition Process control Passwords Fingerprint recognition Extraterrestrial measurements Real-time systems Pins Reliability

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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

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Chinese Journal of Cancer research 2023年第4期35卷 408-423页

作者： Yuqian Chen Zhen Hua Fan Lin Tiantian Zheng Heng Zhou Shijie Zhang Jing Gao Zhongyi Wang Huafei Shao Wenjuan Li Fengjie Liu Simin Wang Yan Zhang Feng Zhao Hao Liu Haizhu Xie Heng Ma Haicheng Zhang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Department of Radiology Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Fudan University Cancer CenterShanghai 200433China Department of Radiology Guangdong Maternal and Child Health HospitalGuangzhou 510010China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Yizhun Medical AI Co.Ltd. Beijing 100080China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) ***: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ ***: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to

关键词： Deep learning contrast-enhanced mammography breast lesions detection classification

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Prognostication of zooplankton-driven cholera pathoepidemiological Dynamics: Novel Bayesian-regularized deep NARX neuroarchitecture

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computers in Biology and Medicine 2025年 192卷 110197-110197页

作者： Raja, Muhammad Junaid Ali Asif Sultan, Adil Chang, Chuan-Yu Shu, Chi-Min Kiani, Adiqa Kausar Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan AI Center Yuan Ze University Taoyuan320 Taiwan

Background: Cholera outbreaks pose significant health concerns, particularly through freshwater contamination through zooplankton serving as reservoirs for Vibrio Cholerae. Understanding these complex interactions within the aquatic ecosystem through mathematical modeling regimes may help us predict and prevent the spread of Cholera disease spread in affected regions. Method: In this study, an innovative Bayesian regularized deep nonlinear autoregressive exogenous (BRDNARX) neural networks are employed to model the intricate dynamics of Zooplankton-Driven Cholera Disease Transmission (ZDCDT) system. The cholera epidemic propagation through freshwater contamination is uncovered with analysis on densities of phytoplankton, vibrio cholerae carrying zooplankton, human population vector and microbial pathogen vector populous in the marine biosphere. Synthetic data for the ZDCDT is presented for diverse simulations using a modified Adams-Bashforth-Moulton predictor corrector numerical scheme. Subsequently, these temporal data sequences are preprocessed for the novel BRDNARX computing paradigm with an exhaustive assessment on mean square error iterative convergence plots, error histogram charts, regression index reports, input-error crosscorrelation charts, error autocorrelation charts, and time-series response dynamics. Results and conclusions: Comparative absolute error analysis with reference numerical solution adheres to diminutive disparities of range 10−3 to 10−9. Finally, BRDNARX neurostructures are reconfigured for predictive analysis of ZDCDT system in terms of single and multi-step ahead predictors with mean square error outcomes that range from 10−9 to 10−11. This establishes the efficacy of BRDNARX in correctly adhering to the intricacies of the zooplankton-driven cholera pathoepidemiological dynamics with precise forward prognostication. © 2025 Elsevier Ltd

关键词： Mean square error

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