COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features

Heliyon, Год журнала: 2024, Номер 10(5), С. e27509 - e27509

Опубликована: Март 1, 2024

Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis clinical data. Lung imaging, particularly from CT scans, plays pivotal role in identifying and assessing severity COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly reducing diagnostic burden associated with this process. This research aims developing simple DAS for using pre-trained lightweight (LDMs) applied lung slices. The use LDMs contributes less complex yet highly system. key stages developed include image collection initial processing Shannon's thresholding, deep-feature mining supported by LDMs, feature optimization utilizing Brownian Butterfly Algorithm (BBA), binary classification three-fold cross-validation. performance evaluation scheme involves individual, fused, ensemble features. investigation reveals that achieves accuracy 93.80% individual features, 96% fused an impressive 99.10% These outcomes affirm effectiveness enhancing chosen database.

Язык: Английский

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COVID-19 detection from Chest X-ray images using a novel lightweight hybrid CNN architecture

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Май 21, 2024

Язык: Английский

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A tree-based explainable AI model for early detection of Covid-19 using physiological data

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Июнь 24, 2024

Abstract With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) Data Science techniques for disease detection. Although cases declined, there are still deaths around world. Therefore, early detection before onset symptoms has become crucial reducing its extensive impact. Fortunately, wearable devices such as smartwatches proven to be valuable sources physiological data, including Heart Rate (HR) sleep quality, enabling inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts heart rate data predict probability infection symptoms. We train three main model architectures: Gradient Boosting classifier (GB), CatBoost trees, TabNet analyze compare their respective performances. also add interpretability layer our best-performing model, which clarifies prediction results allows a detailed assessment effectiveness. Moreover, created private by gathering from Fitbit guarantee reliability avoid bias. The identical set models was then applied using same pre-trained models, were documented. Using tree-based method, outperformed previous with accuracy 85% on publicly available dataset. Furthermore, produced 81% when You will find source code link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .

Язык: Английский

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A deep learning based architecture for multi-class skin cancer classification

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(39), С. 87105 - 87127

Опубликована: Июль 18, 2024

Язык: Английский

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Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification

Computers in Biology and Medicine, Год журнала: 2024, Номер 180, С. 108971 - 108971

Опубликована: Авг. 5, 2024

Язык: Английский

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