A review on the use of deep learning for medical images segmentation

Neurocomputing, Год журнала: 2022, Номер 506, С. 311 - 335

Опубликована: Июль 28, 2022

Язык: Английский

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A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

Scientific Reports, Год журнала: 2022, Номер 12(1)

Опубликована: Фев. 14, 2022

This study aims to develop an assumption-free data-driven model accurately forecast COVID-19 spread. Towards this end, we firstly employed Bayesian optimization tune the Gaussian process regression (GPR) hyperparameters efficient GPR-based for forecasting recovered and confirmed cases in two highly impacted countries, India Brazil. However, machine learning models do not consider time dependency data series. Here, dynamic information has been taken into account alleviate limitation by introducing lagged measurements constructing investigated models. Additionally, assessed contribution of incorporated features prediction using Random Forest algorithm. Results reveal that significant improvement can be obtained proposed In addition, results highlighted superior performance GPR compared other (i.e., Support vector regression, Boosted trees, Bagged Decision tree, Forest, XGBoost) achieving averaged mean absolute percentage error around 0.1%. Finally, provided confidence level predicted based on showed predictions are within 95% interval. presents a promising shallow simple approach predicting

Язык: Английский

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A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 16

Опубликована: Март 30, 2022

Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which helpful for doctors practitioners. Currently, many deep learning methods are used liver that takes long time to train the model makes this task challenging limited larger hardware resources. In research, we proposed very lightweight convolutional neural network (CNN) extract region CT scan images. The suggested CNN algorithm consists of 3 2 fully connected layers, where softmax discriminate background. Random Gaussian distribution weight initialization achieved distance-preserving-embedding information. known as Ga-CNN (Gaussian-weight CNN). General experiments performed on three benchmark datasets including MICCAI SLiver’07, 3Dircadb01, LiTS17. Experimental results show method well each dataset.

Язык: Английский

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Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey

SN Computer Science, Год журнала: 2022, Номер 3(4)

Опубликована: Май 12, 2022

Язык: Английский

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Wearable technology for early detection of COVID-19: A systematic scoping review

Preventive Medicine, Год журнала: 2022, Номер 162, С. 107170 - 107170

Опубликована: Июль 22, 2022

Язык: Английский

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New and emerging forms of data and technologies: literature and bibliometric review

Multimedia Tools and Applications, Год журнала: 2022, Номер 82(2), С. 2887 - 2911

Опубликована: Июль 30, 2022

With the increased digitalisation of our society, new and emerging forms data present values opportunities for improved driven multimedia services, or even solutions managing future global pandemics (i.e., Disease X). This article conducts a literature review bibliometric analysis existing research records on data. The engages with qualitative search most prominent journal conference publications this topic. statistical software (i.e. R) Web Science records. results are somewhat unexpected. Despite special relationship between US UK, there is not much evidence collaboration in Similarly, despite negative media publicity current China (and sanctions China), topic seems to be growing strong. However, it would interesting repeat exercise after few years compare results. It possible that effect has taken its full yet.

Язык: Английский

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RETRACTED ARTICLE: Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm

Multimedia Tools and Applications, Год журнала: 2023, Номер 82(25), С. 38561 - 38587

Опубликована: Март 22, 2023

Язык: Английский

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Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique

Multimedia Tools and Applications, Год журнала: 2023, Номер 82(22), С. 34163 - 34181

Опубликована: Март 28, 2023

Язык: Английский

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The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Oeconomia Copernicana, Год журнала: 2024, Номер 15(1), С. 27 - 58

Опубликована: Март 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.

Язык: Английский

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Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey

Frontiers in Artificial Intelligence, Год журнала: 2022, Номер 5

Опубликована: Май 27, 2022

Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) CT scan images. These are rich information patterns clusters like structures, which evident conformance detection of pandemics. This paper aims comprehensively study analyze methodology based on Deep for diagnosis. technology is good, practical, affordable modality can be deemed reliable technique adequately virus. Furthermore, research determines potential enhance image character through artificial intelligence distinguishes most inexpensive trustworthy imaging method anticipate dreadful viruses. further discusses cost-effectiveness surveyed methods detecting COVID-19, contrast with other methods. Several finance-related aspects effectiveness different used have discussed. Overall, this presents an overview using their financial implications from perspective insurance claim settlement.

Язык: Английский

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