Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network

Computers in Biology and Medicine, Год журнала: 2025, Номер 186, С. 109659 - 109659

Опубликована: Янв. 22, 2025

Язык: Английский

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Medical Imaging-based Artificial Intelligence in Pneumonia: A Narrative Review

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129731 - 129731

Опубликована: Фев. 1, 2025

Язык: Английский

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Advancing Pulmonary Infection Diagnosis: A Comprehensive Review of Deep Learning Approaches in Radiological Data Analysis

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 13, 2025

Язык: Английский

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Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions

Computer Science Review, Год журнала: 2025, Номер 57, С. 100751 - 100751

Опубликована: Апрель 4, 2025

Язык: Английский

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A Novel Computer-Aided Approach for Predicting COVID-19 Severity Using Hyperparameters in ResNet50v2 from X-ray Images

International Journal of experimental research and review, Год журнала: 2024, Номер 42, С. 120 - 132

Опубликована: Авг. 30, 2024

This research has been globally impacted by COVID-19 virus, which was a very uncommon, highly contagious & dangerous respiratory illness demanding early detection for effective containment and further spread. In this research, we proposed an innovative methodology that utilizes images of X-rays at stage. By employing convolution neural network, enhance the accuracy performance via using ResNet50v2 hyperparameter. The achieves remarkable with average 99.12%. surpasses other available models based on different deep learning like VGG, Xception DenseNet COVID identification help X-ray images. scans are now preferably used modality COVID-19, given its widespread utilization effectiveness. However, manual treatment examination is challenging, specifically in field facing limitation skilled medical staff. Utilization demonstrated significant potential results automating diagnosis timely films. suggested architecture developed prediction analysis cases It firmly believes study holds alleviating workload frontline radiologists, expediting patient treatment, facilitating pandemic control efforts.

Язык: Английский

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Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: Insights from a multi-national chest CT scan study

Computers in Biology and Medicine, Год журнала: 2024, Номер 185, С. 109461 - 109461

Опубликована: Дек. 3, 2024

Язык: Английский

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Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through Deep Transfer Learning Approach

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 22, 2024

Abstract SARS-CoV-2 can infect alveoli, inducing a lung injury and thereby impairing the function. Healthy alveolar type II (AT2) cells play major role in repair as well keeping alveoli space free from fluids, which is not case for infected AT2 cells. Unlike previous studies, this novel study aims to automatically differentiate between healthy with through using efficient AI-based models, aid disease control treatment. Therefore, we introduce highly accurate deep transfer learning (DTL) approach that works follows. First, downloaded processed 286 images pertaining human (hAT2) cells, obtained electron microscopy public image archive. Second, provided two DTL computations induce ten models. The first computation employs five pre-trained models (including DenseNet201 ResNet152V2) trained on more than million ImageNet database extract features hAT2 images. Then, flattening providing output feature vectors densely connected classifier Adam optimizer. second similar manner minor difference freeze layers extraction while unfreezing training next layers. Compared TFtDenseNet201, experimental results five-fold cross-validation demonstrate TFeDenseNet201 12.37 × faster superior, yielding highest average ACC of 0.993 (F1 0.992 MCC 0.986) statistical significance ( p < 2.2 10 −16 t -test).

Язык: Английский

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

Iran Journal of Computer Science, Год журнала: 2024, Номер 7(3), С. 699 - 721

Опубликована: Май 2, 2024

Язык: Английский

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Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Mathematics, Год журнала: 2024, Номер 12(10), С. 1573 - 1573

Опубликована: Май 17, 2024

Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims investigate COVID-19 classification at cellular level in response Particularly, differentiating between healthy and human alveolar type II (hAT2) Hence, we explore feasibility deep transfer learning (DTL) introduce highly accurate approach that works as follows: First, downloaded processed 286 images pertaining hAT2 obtained from public image archive. Second, provided two DTL computations induce ten models. The first computation employs five pre-trained models (including DenseNet201 ResNet152V2) trained on more than one million ImageNet database extract features images. Then, it flattens output feature vectors trained, densely connected classifier Adam optimizer. second similar manner, minor difference freeze layers for extraction while unfreezing jointly training next layers. results using five-fold cross-validation demonstrated TFeDenseNet201 is 12.37× faster superior, yielding highest average ACC 0.993 (F1 0.992 MCC 0.986) statistical significance (P<2.2×10−16 t-test) compared an 0.937 0.938 0.877) counterpart (TFtDenseNet201), showing no (P=0.093 t-test).

Язык: Английский

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Privacy-Centric Multi-Class Detection of COVID 19 Through Breathing Sounds and Chest X-Ray Images: Blockchain and Optimized Neural Networks

IEEE Access, Год журнала: 2024, Номер 12, С. 89968 - 89985

Опубликована: Янв. 1, 2024

Язык: Английский

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