ETSVF-COVID19: efficient two-stage voting framework for COVID-19 detection

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(29), P. 18277 - 18295

Published: July 24, 2024

Abstract COVID-19 disease, an outbreak in the spring of 2020, reached very alarming dimensions for humankind due to many infected patients during pandemic and heavy workload healthcare workers. Even though we have been saved from darkness after about three years, importance computer-aided automated systems that support field experts fight against with global threat has emerged once again. This study proposes a two-stage voting framework called ETSVF-COVID19 includes transformer-based deep features machine learning approach detecting disease. ETSVF-COVID19, which offers 99.2% 98.56% accuracies on computed tomography scan X-radiation images, respectively, could compete related works literature. The findings demonstrate this assist making informed decisions while diagnosing its fast accurate classification role. Moreover, screen chest infections help physicians, particularly areas where test kits specialist doctors are inadequate.

Language: Английский

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Predicting human trust in human-robot collaborations using machine learning and psychophysiological responses

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102720 - 102720

Published: July 19, 2024

Language: Английский

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Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults

Journal of Digital Imaging, Journal Year: 2023, Volume and Issue: 36(5), P. 2035 - 2050

Published: June 7, 2023

Language: Английский

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MRI-Based Brain Tumor Classification Using a Dilated Parallel Deep Convolutional Neural Network

Digital, Journal Year: 2024, Volume and Issue: 4(3), P. 529 - 554

Published: June 28, 2024

Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) to better comprehend the spatial connections among pixels in complex pictures. Due their tiny receptive fields, majority of deep network (DCNN)-based techniques overfit and unable extract global context information from more significant regions. While dilated convolution retains data resolution at output layer increases field without adding computation, stacking several convolutions has drawback producing a grid effect. This research suggests parallel (PDCNN) architecture that preserves wide order handle gridding artifacts both coarse fine features images. article applies multiple preprocessing strategies input MRI images used train model. By contrasting various dilation rates, path uses low rate (2,1,1), while local (4,2,1) for decremental even numbers tackle two paths. Using three different types datasets, suggested PDCNN average ensemble method performs best. The achieved multiclass Kaggle dataset-III, Figshare dataset-II, binary tumor identification dataset-I is 98.35%, 98.13%, 98.67%, respectively. In comparison state-of-the-art techniques, structure improves results by extracting features, making it efficient.

Language: Английский

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Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis COVID-19 cases. As imaging technologies have advanced, 3D CNNs emerged as a powerful tool for segmenting classifying in medical images. These demonstrated both high accuracy rapid capabilities, making them crucial effective diagnostics. This study offers thorough review various CNN algorithms, evaluating their efficacy across range modalities. systematically examines recent advancements methodologies. process involved comprehensive screening abstracts titles to ensure relevance, followed by meticulous selection research papers from academic repositories. evaluates these based on specific criteria provides detailed insights into network architectures algorithms used detection. reveals significant trends use segmentation classification. It highlights key findings, including diverse employed compared other diseases, which predominantly utilize encoder/decoder frameworks. an in-depth methods, discussing strengths, limitations, potential areas future research. reviewed total 60 published repositories, Springer Elsevier. this implications clinical diagnosis treatment strategies. Despite some efficiency underscore advancing image findings suggest that could significantly enhance management COVID-19, contributing improved healthcare outcomes.

Language: Английский

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Detection of high-risk diseases in poultry feces through transfer learning

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 64, P. 102002 - 102002

Published: Feb. 21, 2025

Language: Английский

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Predictive modeling of combustion cycle variations in spark ignition engine based on backpropagation neural network and artificial bee colony algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110813 - 110813

Published: April 11, 2025

Language: Английский

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An Automated Vertebrae Localization, Segmentation, and Osteoporotic Compression Fracture Detection Pipeline for Computed Tomographic Imaging

Deleted Journal, Journal Year: 2024, Volume and Issue: 37(5), P. 2428 - 2443

Published: May 8, 2024

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) type of osteoporotic fracture. Approximately 700,000 VCFs are diagnosed annually in USA alone, resulting an annual economic burden ~$13.8B. With aging population, rate and their associated burdens expected to rise. Those include pain, functional impairment, increased medical expenditure. Therefore, it utmost importance develop analytical tool aid identification VCFs. Computed Tomography (CT) imaging commonly used detect occult injuries. Unlike existing VCF detection approaches based on CT, standard clinical criteria for determining relies shape vertebrae, such as loss vertebral body height. We developed a novel automated vertebrae localization, segmentation, pipeline CT scans using state-of-the-art deep learning models bridge this gap. To do so, we employed publicly available dataset spine with 325 annotated 126 which also graded (81 45 without VCFs). Our approach attained 96% sensitivity 81% specificity detecting at vertebral-level, 100% accuracy subject-level, outperforming counterparts tested segmentation. Crucially, showed that adding predicted segments inputs significantly improved both subject levels by up 14% Sensitivity 20% Specificity (p-value = 0.028).

Language: Английский

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Classification of Thoracic X-Ray Images of COVID-19 Patients Using the Convolutional Neutral Network (CNN) Method

Journal of Computer Science, Journal Year: 2024, Volume and Issue: 20(4), P. 357 - 364

Published: Feb. 15, 2024

Recently, radiology modalities have been widely used to detect COVID-19. Thoracic X-rays and CT scans are the primary radiological tools utilized in diagnosis treatment of individuals with In addition, chest more accurate sensitive early COVID-19 identification. A new problem arises diagnosing results scan images by radiologists or specialists where is difficult distinguish from pneumonia caused other viruses bacteria, so misdiagnosis can occur. Many researchers worldwide developed computer-aided detection schemes based on medical image processing machine learning overcome this challenge. This research focuses development previous studies, use Convolutional Neural Network (CNN) method classify X-ray Images Patients compared model Roboflow. Image manipulation techniques applied study pseudo color program Python. employs technique uses data patients confirmed at Andalas University Hospital 2022. Based study's results, a very good CNN Specificity score 93% was obtained perfect Sensitivity value produced using Roboflow model, which 100%. However, Kappa for both methods below expected threshold 36-38%. ROC value, calculating normal patients.

Language: Английский

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Machine learning investigation of optimal psychoemotional well-being factors for students’ reading literacy

Education and Information Technologies, Journal Year: 2024, Volume and Issue: 29(14), P. 18257 - 18285

Published: March 8, 2024

Language: Английский

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