Exploring COVID-19 Classification and Object Detection Strategies DOI
Saifullah Jan,

Aiman a e - ed - d e-a d - ffcc e cb,

Bilal Khan

et al.

Advances in geospatial technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 198 - 218

Published: June 7, 2024

The overlapping imaging characteristics of COVID-19 viral pneumonia and non-COVID-19 chest X-rays (CXRs) make differentiation difficult for radiologists. Machine learning (ML) has demonstrated promising outcomes in a range medical sectors, enhancing diagnostic accuracy through its interaction with radiological tests. potential contribution ML models assisting radiologists discriminating from CXRs, on the other hand, deserves further examination exploration. goal this study is to empirically assess models' capacity classify X-ray images into COVID-19, pneumonia, normal cases. evaluates efficacy K-nearest Neighbor (KNN), random forest (RF), AdaBoost (AB), neural networks (NN) various hidden neuron configurations using wide performance measures. These metrics evaluate area under curve (AUC), classification (CA), F1 score (F1), precision, recall, resulting comprehensive evaluation technique. ROC analysis used gain thorough knowledge skills. results show that NN models, particularly those 100 150 neurons, outperform all criteria, proving their ability reliably categorize disorders. Notably, emphasizes difficulties separating emphasizing importance strong methods. While provides useful insights, drawbacks include use single dataset, absence more sophisticated deep architectures, lack interpretability analyses. Nonetheless, adds developing picture categorization, directing future attempts improve diagnosis widen machine healthcare. findings highlight utility diagnostics pave way vital technology

Language: Английский

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

55
Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis DOI Creative Commons
Ghazi Mauer Idroes, Teuku Rizky Noviandy, Aga Maulana

et al.

Journal of Educational Management and Learning, Journal Year: 2023, Volume and Issue: 1(1), P. 8 - 15

Published: July 24, 2023

Artificial intelligence (AI) has emerged as a powerful technology that the potential to transform education. This study aims comprehensively understand students' perspectives on using AI within educational settings gain insights about role of in education and investigate their perceptions regarding advantages, challenges, expectations associated with integrating into learning process. We analyzed student responses from survey targeted students diverse academic backgrounds levels. The results show that, general, have positive perception believe is beneficial for However, they are still concerned some drawbacks AI. Therefore, it necessary take steps minimize negative impact while continuing advantage advantages

Language: Английский

Citations

33
Researching public health datasets in the era of deep learning: a systematic literature review DOI Creative Commons
Rand Obeidat, Izzat Alsmadi, Qanita Bani Baker

et al.

Health Informatics Journal, Journal Year: 2025, Volume and Issue: 31(1)

Published: Jan. 1, 2025

Objective: Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, then understand the current landscape. Materials Methods: A systematic literature review was conducted June 2023 to search articles on data context of learning, published from inception medical computer science databases through 2023. The focused diverse datasets, abstracting applications, challenges, advancements learning. Results: 2004 were reviewed, identifying 14 disease categories. Observed trends include explainable-AI, patient embedding integrating different sources employing models informatics. Noted technical reproducibility handling sensitive data. Discussion: There has been a notable surge publications since 2015. Consistent continue be applied across Despite wide standard approach still does not exist addressing outstanding issues this field. Conclusion: Guidelines are needed applying improve FAIRness, efficiency, transparency, comparability, interoperability research. Interdisciplinary collaboration among scientists, experts, policymakers is harness full potential

Language: Английский

Citations

1
Systematic reviews of machine learning in healthcare: a literature review DOI Creative Commons
Katarzyna Kolasa,

Bisrat Yeshewas Admassu,

Malwina Hołownia-Voloskova

et al.

Expert Review of Pharmacoeconomics & Outcomes Research, Journal Year: 2023, Volume and Issue: 24(1), P. 63 - 115

Published: Nov. 13, 2023

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

Language: Английский

Citations

14
Challenges, opportunities, and advances related to COVID-19 classification based on deep learning DOI Creative Commons
Abhishek Agnihotri,

Narendra Kohli

Data Science and Management, Journal Year: 2023, Volume and Issue: 6(2), P. 98 - 109

Published: March 31, 2023

The novel coronavirus disease, or COVID-19, is a hazardous disease. It endangering the lives of many people living in more than two hundred countries. directly affects lungs. In general, main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve speedy reliable medical diagnosis. Identifying images exceedingly difficult for diagnosis, assessment, treatment. demanding, time-consuming, subject human mistakes. biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As subfield AI, deep learning (DL) networks have drawn considerable attention standard machine (ML) methods. DL models automatically carry out all steps feature extraction, selection, classification. This study has performed comprehensive analysis classification using CXR CT modalities architectures. Additionally, we discussed how transfer helpful this regard. Finally, problem designing implementing system computer-aided diagnostic (CAD) find COVID-19 approaches highlighted future research possibility.

Language: Английский

Citations

13
Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review DOI Creative Commons

Lunlin Fei,

Bing Han

Sensors, Journal Year: 2023, Volume and Issue: 23(8), P. 3852 - 3852

Published: April 10, 2023

Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by cameras. With the advancement of technology in recent years, it has received a lot attention researchers applications such as intelligent transportation, public safety self-driving driving technology. As result, large number excellent research results have emerged field MOMCT. To facilitate rapid development need to keep abreast latest current challenges related field. Therefore, this paper provide comprehensive review multi-object multi-camera tracking based on deep learning for transportation. Specifically, we first introduce main object detectors MOMCT detail. Secondly, give an in-depth analysis evaluate advanced methods through visualisation. Thirdly, summarize popular benchmark data sets metrics quantitative comparisons. Finally, point out faced transportation present practical suggestions future direction.

Language: Английский

Citations

13
Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm DOI Creative Commons
C. A. Rivera-Romero, Jorge Muñoz‐Minjares, Carlos Lastre-Domínguez

et al.

Big Data and Cognitive Computing, Journal Year: 2024, Volume and Issue: 8(2), P. 13 - 13

Published: Jan. 26, 2024

Identifying patient posture while they are lying in bed is an important task medical applications such as monitoring a after surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy the patient’s position classification of images created from grid pressure sensors located bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning required before loaded into method increase accuracy. However, continuous person requires large amounts time computational resources if complex pre-processing algorithms used. So, problem classify patients with different weights, heights, positions by using minimal sample specific method. In this work, it proposed sensor well-known simple techniques selecting optimal texture descriptors Support Vector Machine (SVM) This order obtain best avoid over-processing stage SVM. The experimental stages performed color models Red, Green, Blue (RGB) Hue, Saturation, Value (HSV). results show accuracy 86.9% 92.9% kappa value 0.825 0.904 histogram equalization median filter, respectively.

Language: Английский

Citations

4
A three layer stacked multimodel transfer learning approach for deep feature extraction from Chest Radiographic images for the classification of COVID-19 DOI
Baijnath Kaushik, Akshma Chadha, Abhigya Mahajan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110241 - 110241

Published: Feb. 25, 2025

Language: Английский

Citations

0
A Deep Feature Ensemble Methodology for 2D Biomedical Image Classification DOI

Rajat Rajoria,

Balmukund Kanodia,

Debam Saha

et al.

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 85 - 99

Published: Jan. 1, 2025

Language: Английский

Citations

0
Integrating pyramid vision transformer and topological data analysis for brain tumor DOI Creative Commons
Dhananjay Joshi, Bhupesh Kumar Singh,

Kapil Kumar Nagwanshi

et al.

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 7

Published: April 10, 2025

Introduction Brain tumor (BT) classification is crucial yet challenging due to the complex and varied nature of these tumors. We present a novel approach combining Pyramid Vision Transformer (PVT) with an adaptive deformable attention mechanism Topological Data Analysis (TDA) address complexities BT detection. While PVT have been explored in prior work, we introduce key innovations enhance their performance for medical image analysis. Methods developed that dynamically adjusts receptive fields based on complexity, focusing critical regions MRI scans. The also incorporates sampling rate hierarchical dynamic position embeddings context-aware multi-scale feature extraction. Feature channels are partitioned into specialized groups via offset group improve diversity, strategy further integrates local global contexts yield refined representations. Additionally, applying TDA images extracts meaningful topological patterns, followed by Random Forest classifier final classification. Results method was evaluated Figshare brain dataset. It achieved 99.2% accuracy, 99.35% recall, 98.9% precision, 99.12% F1-score, Matthews correlation coefficient (MCC) 0.98, LogLoss 0.05, average processing time approximately 6 seconds per image. Discussion These results underscore method's ability combine detailed extraction insights, significantly improving accuracy efficiency proposed offers promising tool more reliable rapid diagnosis.

Language: Английский

Citations

0