Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review

Sustainable Cities and Society, Journal Year: 2022, Volume and Issue: 85, P. 104089 - 104089

Published: July 23, 2022

Language: Английский

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Machine learning applications for COVID-19 outbreak management

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 34(18), P. 15313 - 15348

Published: June 10, 2022

Language: Английский

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

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

Published: Jan. 26, 2024

Language: Английский

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Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis

Diagnostics, Journal Year: 2023, Volume and Issue: 13(4), P. 686 - 686

Published: Feb. 12, 2023

Cervical cancer is one of the most common types among women, which has higher death-rate than many other types. The way to diagnose cervical analyze images cells, performed using Pap smear imaging test. Early and accurate diagnosis can save lives patients increase chance success treatment methods. Until now, various methods have been proposed based on analysis images. Most existing be divided into two groups deep learning techniques or machine algorithms. In this study, a combination method presented, whose overall structure strategy, where feature extraction stage completely separate from classification stage. However, in stage, networks are used. paper, multi-layer perceptron (MLP) neural network fed with features presented. number hidden layer neurons tuned four innovative ideas. Additionally, ResNet-34, ResNet-50 VGG-19 used feed MLP. presented method, layers related phase removed these CNN networks, outputs MLP after passing through flatten layer. order improve performance, both CNNs trained Adam optimizer. evaluated Herlev benchmark database provided 99.23 percent accuracy for two-classes case 97.65 7-classes case. results shown that baseline

Language: Английский

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Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 4, 2024

Abstract The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this has several drawbacks, including high cost, lengthy turnaround time results, and the potential false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed diagnosing disease. Chest are more commonly than CT scans widespread availability of X-ray machines, lower ionizing radiation, cost equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary radiologists manually search biomarkers. process time-consuming prone errors. Therefore, there a critical need develop an automated system evaluating X-rays. Deep learning techniques expedite process. In study, deep learning-based called Custom Convolutional Neural Network (Custom-CNN) proposed identifying infection in Custom-CNN model consists eight weighted layers utilizes strategies like dropout batch normalization enhance performance reduce overfitting. approach achieved classification accuracy 98.19% aims accurately classify COVID-19, normal, pneumonia samples.

Language: Английский

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COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network

Journal of Applied Biomedicine, Journal Year: 2022, Volume and Issue: 43(1), P. 1 - 16

Published: Nov. 24, 2022

Language: Английский

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Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression

Life, Journal Year: 2023, Volume and Issue: 13(3), P. 691 - 691

Published: March 3, 2023

Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, disease monitoring. Logistic regression YOLOv4 popular algorithms that be used for these tasks. However, techniques have limitations performance issue big medical data. In this study, we presented a robust approach big-medical-data using logistic YOLOv4, respectively. To improve algorithms, proposed use advanced parallel k-means pre-processing, clustering technique identified patterns structures Additionally, leveraged acceleration capabilities neural engine processor to further enhance speed efficiency our approach. We evaluated on several large datasets showed it could accurately classify amounts data detect images. Our results demonstrated combination resulted significant improvement making them more reliable applications. This new offers promising solution may implications healthcare.

Language: Английский

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A survey on deep learning models for detection of COVID-19

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(23), P. 16945 - 16973

Published: May 27, 2023

Language: Английский

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OPT-CO: Optimizing pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks

Information Sciences, Journal Year: 2024, Volume and Issue: 680, P. 121141 - 121141

Published: July 8, 2024

Building upon pre-trained ViT models, many advanced methods have achieved significant success in COVID-19 classification. Many scholars pursue better performance by increasing model complexity and parameters. While these can enhance performance, they also require extensive computational resources extended training times. Additionally, the persistent challenge of overfitting, due to limited dataset sizes, remains a hurdle. To address challenges, we proposed novel method optimize transformer models for efficient classification with stochastic configuration networks (SCNs), referred as OPT-CO. We two optimization methods: sequential (SeOp) parallel (PaOp), incorporating optimizers manner, respectively. Our without necessitating parameter expansion. introduced OPT-CO-SCN avoid overfitting problems through adoption random projection head augmentation. The experiments were carried out evaluate our based on publicly available datasets. Based evaluation results, superior, surpassing other state-of-the-art methods.

Language: Английский

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Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(8), P. 176 - 176

Published: July 23, 2024

This paper addresses the significant problem of identifying relevant background and contextual literature related to deep learning (DL) as an evolving technology in order provide a comprehensive analysis application DL specific pneumonia detection via chest X-ray (CXR) imaging, which is most common cost-effective imaging technique available worldwide for diagnosis. particular key period associated with COVID-19, 2020–2023, explain, analyze, systematically evaluate limitations approaches determine their relative levels effectiveness. The context applied both aid automated substitute existing expert radiography professionals, who often have limited availability, elaborated detail. rationale undertaken research provided, along justification resources adopted relevance. explanatory text subsequent analyses are intended sufficient detail being addressed, solutions, these, ranging from more general. Indeed, our evaluation agree generally held view that use transformers, specifically, vision transformers (ViTs), promising obtaining further effective results area using CXR images. However, ViTs require extensive address several limitations, specifically following: biased datasets, data code ease model can be explained, systematic methods accurate comparison, notion class imbalance possibility adversarial attacks, latter remains fundamental research.

Language: Английский

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