ArticleJournal of King Saud University. Computer and information sciences2022
Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images.
Article in Journal of King Saud University. Computer and information sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 33 citations in OpenAlex.
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- Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images.Journal of imaging informatics in medicine · 2025Article
- Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification.BMC medical informatics and decision making · 2025Article
- Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.Scientific reports · 2025Article
- Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.BMC medical informatics and decision making · 2025Article
- A Hybrid Deep Transfer Learning Framework for Delamination Identification in Composite Laminates.Sensors (Basel, Switzerland) · 2025Article
- Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm.Scientific reports · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting.Diagnostics (Basel, Switzerland) · 2023Article
- Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests.Frontiers in plant science · 2023Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Coronavirus disease is quickly spreading all over the world and the emergency situation is still out of control. Latest achievements of deep learning algorithms suggest the use of deep Convolutional Neural Network to implement a computer-aided diagnostic system for automatic classification of COVID-19 CT images. In this paper, we propose to employ a feature-wise attention layer in order to enhance the discriminative features obtained by convolutional networks. Moreover, the original performance of the network has been improved using the mixup data augmentation technique. This work compares the proposed attention-based model against the stacked attention networks, and traditional versus mixup data augmentation approaches. We deduced that feature-wise attention extension, while outperforming the stacked attention variants, achieves remarkable improvements over the baseline convolutional neural networks. That is, ResNet50 architecture extended with a feature-wise attention layer obtained 95.57% accuracy score, which, to best of our knowledge, fixes the state-of-the-art in the challenging COVID-CT dataset.
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