ArticleIntelligent systems with applications2023
Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans.
Article in Intelligent systems with applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Advanced COVID-19 detection using cough signals with space reconstruction and 3D deep convolutional neural networks.Scientific reports · 2025Article
- A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis.Tomography (Ann Arbor, Mich.) · 2024Review
- Dynamic modeling of photoacoustic sensor data to classify human blood samples.Medical & biological engineering & computing · 2024Article
- Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.Journal of biomedical optics · 2023Article
- Contemporary Role and Applications of Artificial Intelligence in Dentistry.F1000Research · 2023Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes.
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