ArticleComputers in biology and medicine2021
A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
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- Novel deep learning for multi-class classification of Alzheimer's in disability using MRI datasets.Frontiers in bioinformatics · 2025Article
- Predicting malignancy in breast lesions: enhancing accuracy with fine-tuned convolutional neural network models.BMC medical imaging · 2024Article
- Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.Scientific reports · 2024Article
- Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities.Diagnostics (Basel, Switzerland) · 2024Article
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- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images.La Radiologia medica · 2024Article
- A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection.PloS one · 2024Article
- An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.Frontiers in oncology · 2024Article
- Diagnosis and detection of pneumonia using weak-label based on X-ray images: a multi-center study.BMC medical imaging · 2023Article
- Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.Heliyon · 2023Article
- An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques.BMC bioinformatics · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- COVID-19 and human development: An approach for classification of HDI with deep CNN.Biomedical signal processing and control · 2023Article
- Effects of image-sharpening algorithm on surgical field visibility during 3D heads-up surgery for vitreoretinal diseases.Scientific reports · 2023Article
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9 authors.
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Abstract
Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment.
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