ArticleChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society2023
RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.
Article in Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.Scientific reports · 2024Article
- Texture-Based Classification to Overcome Uncertainty between COVID-19 and Viral Pneumonia Using Machine Learning and Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2024Article
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection.Biomimetics (Basel, Switzerland) · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- RiPa-Net: Recognition of Rice Paddy Diseases with Duo-Layers of CNNs Fostered by Feature Transformation and Selection.Biomimetics (Basel, Switzerland) · 2023Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 diagnosis utilizing wavelet-based contrastive learning with chest CT images.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.Healthcare (Basel, Switzerland) · 2023Article
- COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans.Bioengineering (Basel, Switzerland) · 2023Article
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Abstract
Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The spatial information in these images was used to train DL models in the majority of relevant studies. However, training these models with images generated by radiomics approaches could enhance diagnostic accuracy. Furthermore, combining information from several radiomics approaches with time-frequency representations of the COVID-19 patterns can increase performance even further. This study introduces "RADIC", an automated tool that uses three DL models that are trained using radiomics-generated images to detect COVID-19. First, four radiomics approaches are used to analyze the original CT and X-ray images. Next, each of the three DL models is trained on a different set of radiomics, X-ray, and CT images. Then, for each DL model, deep features are obtained, and their dimensions are decreased using the Fast Walsh Hadamard Transform, yielding a time-frequency representation of the COVID-19 patterns. The tool then uses the discrete cosine transform to combine these deep features. Four classification models are then used to achieve classification. In order to validate the performance of RADIC, two benchmark datasets (CT and X-Ray) for COVID-19 are employed. The final accuracy attained using RADIC is 99.4% and 99% for the first and second datasets respectively. To prove the competing ability of RADIC, its performance is compared with related studies in the literature. The results reflect that RADIC achieve superior performance compared to other studies. The results of the proposed tool prove that a DL model can be trained more effectively with images generated by radiomics techniques than the original X-Ray and CT images. Besides, the incorporation of deep features extracted from DL models trained with multiple radiomics approaches will improve diagnostic accuracy.
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