1. Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans
- Author
-
Fadi Dornaika, Abdenour Hadid, Abdelkrim Ouafi, Abdelmalik Taleb-Ahmed, Fares Bougourzi, Cosimo Distante, Institute of Applied Sciences and Intelligent Systems (ScienceApp) (ISASI), University of Mohamed Khider [Biskra], Universidad del Pais Vasco / Euskal Herriko Unibertsitatea [Espagne] (UPV/EHU), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), COMmunications NUMériques - IEMN (COMNUM - IEMN), Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Istituto di Scienze Applicate e Sistemi Intelligenti 'Eduardo Caianiello' (ISASI), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), University of Biskra Mohamed Khider, Université catholique de Lille (UCL)-Université catholique de Lille (UCL), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and The authors would like to thank Arturo Argentieri from CNR-ISASI Italy for his support on the multi-GPU computing facilities.
- Subjects
Mean squared error ,Computer science ,Computer applications to medicine. Medical informatics ,R858-859.7 ,convolutional neural network ,02 engineering and technology ,Convolutional neural network ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,symbols.namesake ,[SPI]Engineering Sciences [physics] ,0302 clinical medicine ,Huber loss ,Intensive care ,Photography ,0202 electrical engineering, electronic engineering, information engineering ,Radiology, Nuclear Medicine and imaging ,Electrical and Electronic Engineering ,TR1-1050 ,Estimation ,business.industry ,Deep learning ,COVID-19 ,deep learning ,Pattern recognition ,QA75.5-76.95 ,Computer Graphics and Computer-Aided Design ,Pearson product-moment correlation coefficient ,3. Good health ,Electronic computers. Computer science ,Benchmark (computing) ,symbols ,dataset generation ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,Artificial intelligence ,CT-scans ,business - Abstract
COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state.
- Published
- 2021
- Full Text
- View/download PDF