Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches

Journal of Medical Systems, Journal Year: 2022, Volume and Issue: 46(11)

Published: Oct. 6, 2022

Language: Английский

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A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

Alexandria Engineering Journal, Journal Year: 2022, Volume and Issue: 64, P. 923 - 935

Published: Nov. 2, 2022

In 2019, the world experienced rapid outbreak of Covid-19 pandemic creating an alarming situation worldwide. The virus targets respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed pneumonia, lung cancer, or TB. Thus, early diagnosis COVID-19 is critical since disease provoke patients' mortality. Chest X-ray (CXR) commonly employed in healthcare sector where both quick precise supplied. Deep learning algorithms have proved extraordinary capabilities terms diseases detection classification. They facilitate expedite process save time for medical practitioners. this paper, a deep (DL) architecture multi-class classification Pneumonia, Lung Cancer, tuberculosis (TB), Opacity, most recently proposed. Tremendous CXR images 3615 COVID-19, 6012 opacity, 5870 20,000 1400 tuberculosis, 10,192 normal were resized, normalized, randomly split to fit DL requirements. classification, we utilized pre-trained model, VGG19 followed by three blocks convolutional neural network (CNN) feature extraction fully connected at stage. experimental results revealed that our proposed + CNN outperformed existing work 96.48 % accuracy, 93.75 recall, 97.56 precision, 95.62 F1 score, 99.82 area under curve (AUC). model delivered superior performance allowing practitioners diagnose treat patients more quickly efficiently.

Language: Английский

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COVID-19 image classification using deep learning: Advances, challenges and opportunities

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 144, P. 105350 - 105350

Published: March 3, 2022

Language: Английский

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COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

International Journal of Environmental Research and Public Health, Journal Year: 2021, Volume and Issue: 18(21), P. 11086 - 11086

Published: Oct. 21, 2021

In the recent pandemic, accurate and rapid testing of patients remained a critical task in diagnosis control COVID-19 disease spread healthcare industry. Because sudden increase cases, most countries have faced scarcity low rate testing. Chest X-rays been shown literature to be potential source for patients, but manually checking X-ray reports is time-consuming error-prone. Considering these limitations advancements data science, we proposed Vision Transformer-based deep learning pipeline detection from chest X-ray-based imaging. Due lack large sets, collected three open-source sets images aggregated them form 30 K image set, which largest publicly available collection this domain our knowledge. Our transformer model effectively differentiates normal with an accuracy 98% along AUC score 99% binary classification task. It distinguishes COVID-19, normal, pneumonia patient’s 92% Multi-class For evaluation on fine-tuned some widely used models literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, DenseNet-121, as baselines. outperformed terms all metrics. addition, Grad-CAM based visualization created makes approach interpretable by radiologists can monitor progression affected lungs, assisting healthcare.

Language: Английский

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Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

Healthcare, Journal Year: 2023, Volume and Issue: 11(11), P. 1561 - 1561

Published: May 26, 2023

Pneumonia has been directly responsible for a huge number of deaths all across the globe. shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in way chest X-ray images are acquired and processed, impact quality consistency images. This challenging develop robust algorithms that accurately identify pneumonia types Hence, need robust, data-driven trained on large, high-quality datasets validated using range imaging techniques expert radiological analysis. In this research, deep-learning-based model demonstrated differentiating normal severe cases pneumonia. complete proposed system total eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, MobileNet. These models were simulated two having 5856 112,120 X-rays. The best accuracy obtained MobileNet values 94.23% 93.75% different datasets. Key hyperparameters including batch sizes, epochs, optimizers have considered during comparative interpretation these determine most appropriate model.

Language: Английский

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Fruit classification using attention-based MobileNetV2 for industrial applications

PLoS ONE, Journal Year: 2022, Volume and Issue: 17(2), P. e0264586 - e0264586

Published: Feb. 25, 2022

Recent deep learning methods for fruits classification resulted in promising performance. However, these are with heavy-weight architectures nature, and hence require a higher storage expensive training operations due to feeding large number of parameters. There is necessity explore lightweight models without compromising the accuracy. In this paper, we propose model using pre-trained MobileNetV2 attention module. First, convolution features extracted capture high-level object-based information. Second, an module used interesting semantic The modules then combined together fuse both information information, which followed by fully connected layers softmax layer. Evaluation our proposed method, leverages transfer approach, on three public fruit-related benchmark datasets shows that method outperforms four latest smaller trainable parameters superior Our has great potential be adopted industries closely related fruit growing retailing or processing chain automatic identification classifications future.

Language: Английский

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Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 150, P. 106156 - 106156

Published: Oct. 3, 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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A systematic literature review of machine learning application in COVID-19 medical image classification

Procedia Computer Science, Journal Year: 2023, Volume and Issue: 216, P. 749 - 756

Published: Jan. 1, 2023

Detecting COVID-19 as early possible and quickly is one way to stop the spread of COVID-19. Machine learning development can help diagnose more accurately. This report aims find out how far research has progressed what lessons be learned for future in this sector. By filtering titles, abstracts, content Google Scholar database, literature review was able 19 related papers answer two questions, i.e. medical images are commonly used classification methods classification. According findings, chest X-ray were most data categorize transfer techniques method study. Researchers also concluded that lung segmentation use multimodal could improve performance.

Language: Английский

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Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs

Procedia Computer Science, Journal Year: 2024, Volume and Issue: 235, P. 1841 - 1850

Published: Jan. 1, 2024

Diagnosing lung inflammation, particularly pneumonia, is of paramount importance for effectively treating and managing the disease. Pneumonia a common respiratory infection caused by bacteria, viruses, or fungi can indiscriminately affect people all ages. As highlighted World Health Organization (WHO), this prevalent disease tragically accounts substantial 15% global mortality in children under five years age. This article presents comparative study Inception-ResNet deep learning model's performance diagnosing pneumonia from chest radiographs. The leverages Mendeley's X-ray images dataset, which contains 5856 2D images, including both Viral Bacterial images. model compared with seven other state-of-the-art convolutional neural networks (CNNs), experimental results demonstrate superiority extracting essential features saving computation runtime. Furthermore, we examine impact transfer fine-tuning improving models. provides valuable insights into using models diagnosis highlights potential field. In classification accuracy, Inception-ResNet-V2 showed superior to models, ResNet152V2, MobileNet-V3 (Large Small), EfficientNetV2 InceptionV3, NASNet-Mobile, margins. It outperformed them 2.6%, 6.5%, 7.1%, 13%, 16.1%, 3.9%, 1.6%, respectively, demonstrating its significant advantage accurate classification.

Language: Английский

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