A Survey on Medical Image Segmentation Based on Deep Learning Techniques DOI Creative Commons

Jayashree Moorthy,

Usha Devi Gandhi

Big Data and Cognitive Computing, Journal Year: 2022, Volume and Issue: 6(4), P. 117 - 117

Published: Oct. 17, 2022

Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep field, including major common issues published recent years, and also discusses fundamentals of concepts applicable to The study can be applied categorization, object recognition, segmentation, registration, other tasks. First, basic ideas techniques, applications, frameworks are introduced. that operate ideal applications briefly explained. paper indicates there is previous experience with class has been designed describe respond various challenges field analysis such low accuracy classification, segmentation resolution, poor enhancement. Aiming solve these present improve evolution challenges, we provide suggestions future research.

Language: Английский

A comprehensive survey on the biometric systems based on physiological and behavioural characteristics DOI

Shaymaa Adnan Abdulrahman,

Bilal Alhayani

Materials Today Proceedings, Journal Year: 2021, Volume and Issue: 80, P. 2642 - 2646

Published: July 14, 2021

Language: Английский

Citations

60
Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study DOI Open Access
Giovanni Improta, Anna Borrelli, Maria Triassi

et al.

International Journal of Environmental Research and Public Health, Journal Year: 2022, Volume and Issue: 19(9), P. 5215 - 5215

Published: April 26, 2022

Background: In health, it is important to promote the effectiveness, efficiency and adequacy of services provided; these concepts become even more in era COVID-19 pandemic, where efforts manage disease have absorbed all hospital resources. The emergency led a profound restructuring—in very short time—of Italian system. Some factors that impose higher costs on hospitals are inappropriate hospitalization length stay (LOS). (LOS) useful parameter for management within an index evaluated costs. Methods: This study analyzed how changed activity Complex Operative Unit (COU) Neurology Stroke San Giovanni di Dio e Ruggi d’Aragona University Hospital Salerno (Italy). methodology used this was Lean Six Sigma. Problem solving Sigma DMAIC roadmap, characterized by five operational phases. To add value processing, single clinical case, represented stroke patients, investigated verify specific impact pandemic. Results: results obtained show reduction LOS patients increase diagnosis related group relative weight. Conclusions: work has shown how, thanks implementation protocols COU Unit, doctors improved, evident from values parameters taken into consideration.

Language: Английский

Citations

57
Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks DOI
Sahebgoud Hanamantray Karaddi, Lakhan Dev Sharma

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 211, P. 118650 - 118650

Published: Aug. 27, 2022

Language: Английский

Citations

55
Malaria parasite detection using deep learning algorithms based on (CNNs) technique DOI

Muqdad Hanoon Dawood Alnussairi,

Abdullahi Abdu İbrahim

Computers & Electrical Engineering, Journal Year: 2022, Volume and Issue: 103, P. 108316 - 108316

Published: Sept. 1, 2022

Language: Английский

Citations

45
A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies DOI
Sachin Kumar

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102253 - 102253

Published: Aug. 9, 2023

Language: Английский

Citations

26
Using artificial intelligence to implement the UN sustainable development goals at higher education institutions DOI
Walter Leal Filho, Priscilla Cristina Cabral Ribeiro, Janaína Mazutti

et al.

International Journal of Sustainable Development & World Ecology, Journal Year: 2024, Volume and Issue: 31(6), P. 726 - 745

Published: March 21, 2024

Artificial intelligence (AI) can significantly contribute to the implementation of United Nations Sustainable Development Goals (SDGs) by offering innovative solutions and enhancing efficiency processes aimed at achieving these goals. There is a perceived need for studies which may look connections. Against this background, paper reports on study that investigated connections between artificial UN higher education institutions. The deployed multi-methods approach. first one was bibliometric analysis publications in topic. second method used an assessment set case studies, illustrate how being among sample universities support efforts implement SDGs survey identifying current future trends. data gathered allow some trends be identified. For instance, there wide range applications AI sustainability High Education Institutions (HEI), chosen terms campus operations greening, outreach community engagement, research, teaching learning, university management. Also, has identified successful examples deployment various contexts, illustrating what are success factors them. Moreover, fact use quite widely spread, likely increase coming years, due greater demand. Finally, also poses several challenges, such as authenticity ethics (case studies), 'lack access software/materials', information technology training myself/my colleagues' (survey). Overall, offers powerful toolset accelerate enhance SDGs. By analysing vast datasets, predicting outcomes, optimising processes, providing new insights, potential address complex challenges across sectors.

Language: Английский

Citations

14
The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals DOI Creative Commons
George Lăzăroiu, Tom Gedeon, Elżbieta Rogalska

et al.

Oeconomia Copernicana, Journal Year: 2024, Volume and Issue: 15(1), P. 27 - 58

Published: March 30, 2024

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.

Language: Английский

Citations

13
Techno-economic and environmental analyses of hybrid renewable energy systems for a remote location employing machine learning models DOI Creative Commons
Dibyendu Roy, Shunmin Zhu, Ruiqi Wang

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 361, P. 122884 - 122884

Published: March 5, 2024

This article offers a detailed investigation into the technical, economic along with environmental performance of four configurations hybrid renewable energy systems (HRESs), aiming at supplying electricity to remote location, Henry Island in India. The study explores combinations involving photovoltaic (PV) panels, wind turbines, biogas generators, batteries, and converters, while evaluating their economic, performance. analysis yield that among all examined, PV, turbine, generator, battery, converter integrated configuration stands out highly favourable results, showcasing minimal value levelized cost (LCOE) $0.4224 per kWh lowest net present (NPC) $6.41 million. However, technical comprising PV battery yields maximum excess output 2,838,968 kWh/yr. Additionally, machine learning techniques are employed analyse data. shows Bilayered Neural Network model achieves exceptional accuracy predicting LCOE, Medium proves be most accurate These findings provide valuable perception design optimisation HRES for off-grid applications regions, taking account aspects.

Language: Английский

Citations

12
A Comprehensive Exploration of Artificial Intelligence Methods for COVID-19 Diagnosis DOI Creative Commons

S. Balasubramaniam,

M Arishma,

Satheesh Kumar K

et al.

EAI Endorsed Transactions on Pervasive Health and Technology, Journal Year: 2024, Volume and Issue: 10

Published: Feb. 21, 2024

INTRODUCTION: The 2019 COVID-19 pandemic outbreak triggered a previously unseen global health crisis demanding accurate diagnostic solutions. Artificial Intelligence has emerged as promising technology for diagnosis, offering rapid and reliable analysis of medical data. OBJECTIVES: This research paper presents comprehensive review various artificial intelligence methods applied the aiming to assess their effectiveness in identifying cases, predicting disease progression differentiating from other respiratory diseases. METHODS: study covers wide range with application analysing diverse data sources like chest x-rays, CT scans, clinical records genomic sequences. also explores challenges limitations implementing AI -based tools, including availability ethical considerations. CONCLUSION: Leveraging AI’s potential healthcare can significantly enhance efficiency management evolves.

Language: Английский

Citations

10
COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms DOI Creative Commons
Rukundo Prince, Zhendong Niu, Zahid Khan

et al.

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Jan. 17, 2024

Abstract Background COVID-19 is a disease that caused contagious respiratory ailment killed and infected hundreds of millions. It necessary to develop computer-based tool fast, precise, inexpensive detect efficiently. Recent studies revealed machine learning deep models accurately using chest X-ray (CXR) images. However, they exhibit notable limitations, such as large amount data train, larger feature vector sizes, enormous trainable parameters, expensive computational resources (GPUs), longer run-time. Results In this study, we proposed new approach address some the above-mentioned limitations. The model involves following steps: First, use contrast limited adaptive histogram equalization (CLAHE) enhance CXR resulting images are converted from CLAHE YCrCb color space. We estimate reflectance chrominance Illumination–Reflectance model. Finally, normalized local binary patterns generated (Cr) YCb classification vector. Decision tree, Naive Bayes, support machine, K-nearest neighbor, logistic regression were used algorithms. performance evaluation on test set indicates superior, with accuracy rates 99.01%, 100%, 98.46% across three different datasets, respectively. probabilistic algorithm, emerged most resilient. Conclusion Our method uses fewer handcrafted features, affordable resources, less runtime than existing state-of-the-art approaches. Emerging nations where radiologists in short supply can adopt prototype. made both coding materials datasets accessible general public for further improvement. Check manuscript’s availability under declaration section access.

Language: Английский

Citations

9