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

Journal of Imaging, Год журнала: 2024, Номер 10(8), С. 176 - 176

Опубликована: Июль 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.

Язык: Английский

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An Integrated Method of Three Convolution Neural Networks Models and Support Vector Machine and Radial Basis Function Classification for Pneumonia Detection in X-Ray Images

Опубликована: Янв. 1, 2025

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Язык: Английский

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Country-Level Assessment of COVID-19 Performance: A Cluster-Based MACONT-CRITIC Analysis

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112762 - 112762

Опубликована: Янв. 1, 2025

Язык: Английский

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MSSFN: A multi-scale sequence fusion network for ct-based diagnosis of pulmonary complications

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129878 - 129878

Опубликована: Март 1, 2025

Язык: Английский

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Graph embedding dimensionality reduction combined with improved APO optimized kELM for pneumonia recognition

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 108, С. 107909 - 107909

Опубликована: Апрель 22, 2025

Язык: Английский

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Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning

Journal of Imaging, Год журнала: 2024, Номер 10(10), С. 250 - 250

Опубликована: Окт. 13, 2024

The global spread of Coronavirus (COVID-19) has prompted imperative research into scalable and effective detection methods to curb its outbreak. early diagnosis COVID-19 patients emerged as a pivotal strategy in mitigating the disease. Automated using Chest X-ray (CXR) imaging significant potential for facilitating large-scale screening epidemic control efforts. This paper introduces novel approach that employs state-of-the-art Convolutional Neural Network models (CNNs) accurate detection. employed datasets each comprised 15,000 images. We addressed both binary (Normal vs. Abnormal) multi-class (Normal, COVID-19, Pneumonia) classification tasks. Comprehensive evaluations were performed by utilizing six distinct CNN-based (Xception, Inception-V3, ResNet50, VGG19, DenseNet201, InceptionResNet-V2) As result, Xception model demonstrated exceptional performance, achieving 98.13% accuracy, 98.14% precision, 97.65% recall, 97.89% F1-score classification, while multi-classification it yielded 87.73% 90.20% an 87.49% F1-score. Moreover, other utilized models, such competitive performance compared with many recent works.

Язык: Английский

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Enhancing the performance of CNN models for pneumonia and skin cancer detection using novel fractional activation function

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112500 - 112500

Опубликована: Ноя. 1, 2024

Язык: Английский

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