Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Expert Systems, Journal Year: 2021, Volume and Issue: 39(3)

Published: July 28, 2021

COVID-19 is the disease evoked by a new breed of coronavirus called severe acute respiratory syndrome 2 (SARS-CoV-2). Recently, has become pandemic infecting more than 152 million people in over 216 countries and territories. The exponential increase number infections rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) machine (ML), which can assist healthcare sector providing quick precise diagnosis. this paper provides comprehensive review most recent DL ML for studies are published from December 2019 until April 2021. In general, includes 200 that been carefully selected publishers, IEEE, Springer Elsevier. We classify research tracks into two categories: present public datasets established extracted different countries. measures used to evaluate methods comparatively analysed proper discussion provided. conclusion, diagnosing outbreak prediction, SVM widely mechanism, CNN mechanism. Accuracy, sensitivity, specificity measurements previous studies. Finally, will guide community on upcoming development inspire their works future development. This

Language: Английский

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Detection and analysis of COVID-19 in medical images using deep learning techniques

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: Oct. 4, 2021

Abstract The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied X-ray CT-scan medical images for the detection COVID-19. In paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, COVID-19 binary classification task. proposed Fast.AI ResNet framework was designed find out best architecture, pre-processing, training parameters models largely automatically. accuracy F1-score were both above 96% in diagnosis using images. addition, transfer overcome insufficient data improve time. multi-class tasks performed by utilizing VGG16 architecture. High 99% achieved from pneumonia. validity algorithms assessed on well-known public datasets. methods have better results than other related literature. our opinion, can help virologists radiologists make a faster struggle against outbreak

Language: Английский

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A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Sensors, Journal Year: 2022, Volume and Issue: 22(7), P. 2726 - 2726

Published: April 1, 2022

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor challenging because morphology factors like size, location, texture, heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework proposed detect categorize brain tumors magnetic resonance images (MRIs). first phase, deep-boosted features space ensemble classifiers (DBFS-EC) scheme effectively MRI from healthy individuals. The feature achieved through customized well-performing convolutional neural networks (CNNs), consequently, fed into machine learning (ML) classifiers. While second new hybrid fusion-based brain-tumor classification approach proposed, comprised both static dynamic with an ML classifier different types. are extracted region-edge net (BRAIN-RENet) CNN, which able learn inconsistent behavior various tumors. contrast, by using histogram gradients (HOG) descriptor. effectiveness validated on two standard benchmark datasets, were collected Kaggle Figshare contain types tumors, including glioma, meningioma, pituitary, normal Experimental results suggest that DBFS-EC detection outperforms accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), AUC-PR (0.9990). scheme, based fusion spaces BRAIN-RENet HOG, outperform state-of-the-art methods significantly terms (0.9913), (0.9906), (99.20%), (0.9909) CE-MRI dataset.

Language: Английский

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A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

Journal of Experimental & Theoretical Artificial Intelligence, Journal Year: 2023, Volume and Issue: 36(8), P. 1779 - 1821

Published: Jan. 12, 2023

The Coronavirus (COVID-19) outbreak in December 2019 has drastically affected humans worldwide, creating a health crisis that infected millions of lives and devastated the global economy. COVID-19 is ongoing, with emergence many new strains. Deep learning (DL) techniques have proven helpful efficiently analysing delineating infectious regions radiological images. This survey paper draws taxonomy deep for detecting infection radiographic imaging modalities Chest X-Ray, Computer Tomography. DL are broadly categorised into classification, segmentation, multi-stage approaches diagnosis at image region-level analysis. These further classified as pre-trained custom-made Convolutional Neural Network architectures. Furthermore, discussion drawn on datasets, evaluation metrics, commercial platforms provided detection. In end, brief look paid to emerging ideas, gaps existing research, challenges developing diagnostic techniques. provides insight promising areas research likely guide community upcoming development COVID-19. will pave way accelerate designing customised DL-based tools effectively dealing variants challenges.

Language: Английский

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Efficient pneumonia detection using Vision Transformers on chest X-rays

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

Published: Jan. 30, 2024

Abstract Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection treatment pneumonia are essential for avoiding complications enhancing clinical results. We can reduce mortality, improve healthcare efficiency, contribute to the global battle against disease has plagued humanity centuries by devising deploying effective methods. Detecting not only medical necessity but also humanitarian imperative technological frontier. Chest X-rays frequently used imaging modality diagnosing pneumonia. This paper examines in detail cutting-edge method detecting implemented on Vision Transformer (ViT) architecture public dataset chest available Kaggle. To acquire context spatial relationships from X-ray images, proposed framework deploys ViT model, which integrates self-attention mechanisms transformer architecture. According our experimentation with Transformer-based framework, it achieves higher accuracy 97.61%, sensitivity 95%, specificity 98% X-rays. The model preferable capturing context, comprehending relationships, processing images have different resolutions. establishes its efficacy as robust solution surpassing convolutional neural network (CNN) based architectures.

Language: Английский

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Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN

Biomedicines, Journal Year: 2024, Volume and Issue: 12(7), P. 1395 - 1395

Published: June 23, 2024

Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex diverse nature of brain tumors. To address challenge, we propose a novel deep residual region-based convolutional neural network (CNN) architecture, called Res-BRNet, using magnetic resonance imaging (MRI) scans. Res-BRNet employs systematic combination regional boundary-based operations within modified spatial blocks. The blocks extract homogeneity, heterogeneity, boundary-related features tumors, while significantly capture local global texture variations. We evaluated performance on challenging dataset collected from Kaggle repositories, Br35H, figshare, containing various categories, including meningioma, glioma, pituitary, healthy images. outperformed standard CNN models, achieving excellent accuracy (98.22%), sensitivity (0.9811), F1-score (0.9841), precision (0.9822). Our results suggest that promising tool classification, with potential to improve efficiency

Language: Английский

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Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images

Pattern Recognition Letters, Journal Year: 2021, Volume and Issue: 153, P. 67 - 74

Published: Dec. 3, 2021

Language: Английский

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Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 133, P. 109906 - 109906

Published: Dec. 7, 2022

Language: Английский

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CoviXNet: A novel and efficient deep learning model for detection of COVID-19 using chest X-Ray images

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 78, P. 103848 - 103848

Published: June 8, 2022

Language: Английский

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COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms

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

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