ArticlePattern recognition letters2021
MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray.
Article in Pattern recognition letters, 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.
- Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms.Bioengineering (Basel, Switzerland) · 2024Article
- Informed-Learning-Guided Visual Question Answering Model of Crop Disease.Plant phenomics (Washington, D.C.) · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Broad learning solution for rapid diagnosis of COVID-19.Biomedical signal processing and control · 2023Article
- Simultaneous Super-Resolution and Classification of Lung Disease Scans.Diagnostics (Basel, Switzerland) · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
- PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al · 2023Article
- Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters.Diagnostics (Basel, Switzerland) · 2022Article
- Design and development of hybrid optimization enabled deep learning model for COVID-19 detection with comparative analysis with DCNN, BIAT-GRU, XGBoost.Computers in biology and medicine · 2022Article
- Detection of COVID-19 using deep learning techniques and classification methods.Information processing & management · 2022Article
- A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases.Biomedical signal processing and control · 2022Article
- Virtual contrast enhancement for CT scans of abdomen and pelvis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images.Biomedical signal processing and control · 2022Article
- A novel method using Covid-19 dataset and machine learning algorithms FOR THE MOST ACCURATE DIAGNOSIS that can be obtained in medical diagnosis.Biomedical signal processing and control · 2022Article
- Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.Quantitative imaging in medicine and surgery · 2022Article
- A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.Computers in biology and medicine · 2022Article
- MDFNet: an unsupervised lightweight network for ear print recognition.Journal of ambient intelligence and humanized computing · 2022Article
- COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.Diagnostics (Basel, Switzerland) · 2022Article
- Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.Neurocomputing · 2022Article
- A deep learning-based framework for detecting COVID-19 patients using chest X-rays.Multimedia systems · 2022Article
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4 authors.
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Abstract
backgroundCOVID-19 has caused 3.34m deaths till 13/May/2021. It is now still causing confirmed cases and ongoing deaths every day.
methodThis study investigated whether fusing chest CT with chest X-ray can help improve the AI's diagnosis performance. Data harmonization is employed to make a homogeneous dataset. We create an end-to-end multiple-input deep convolutional attention network (MIDCAN) by using the convolutional block attention module (CBAM). One input of our model receives 3D chest CT image, and other input receives 2D X-ray image. Besides, multiple-way data augmentation is used to generate fake data on training set. Grad-CAM is used to give explainable heatmap.
resultsThe proposed MIDCAN achieves a sensitivity of 98.10±1.88%, a specificity of 97.95±2.26%, and an accuracy of 98.02±1.35%.
conclusionOur MIDCAN method provides better results than 8 state-of-the-art approaches. We demonstrate the using multiple modalities can achieve better results than individual modality. Also, we demonstrate that CBAM can help improve the diagnosis performance.
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