Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN)
| Autor: | Babukarthik, R. G., Adiga, V. Ananth Krishna, Sambasivam, G., Chandramohan, D., Amudhavel, J. |
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| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Feature extraction Computers and Information Processing 02 engineering and technology Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering General Materials Science Sensitivity (control systems) business.industry Deep learning General Engineering Computed Tomography (CT) Artificial Intelligence (AI) Pattern recognition Gold standard (test) TK1-9971 Chest X-Ray (CXR) Identification (information) Genetic Deep Learning Convolutional Neural Network (GDCNN) Feature (computer vision) Computational and Artificial Intelligence 020201 artificial intelligence & image processing Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business Transfer of learning |
| Zdroj: | Ieee Access IEEE Access, Vol 8, Pp 177647-177666 (2020) IEEE Access |
| ISSN: | 2169-3536 |
| Popis: | Rapid spread of Coronavirus disease COVID-19 leads to severe pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster, and cheaper. This study aims to provide a solution for identifying pneumonia due to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method. In this research, the state-of-the-art techniques used is Genetic Deep Learning Convolutional Neural Network (GDCNN). It is trained from the scratch for extracting features for classifying them between COVID-19 and normal images. A dataset consisting of more than 5000 CXR image samples is used for classifying pneumonia, normal and other pneumonia diseases. Training a GDCNN from scratch proves that, the proposed method performs better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is achieved. Top classification accuracy obtained in this research reveals the best nominal rate in the identification of COVID-19 disease prediction in an unbalanced environment. The novel model proposed for classification proves to be better than the existing models such as ReseNet18, ReseNet50, Squeezenet, DenseNet-121, and Visual Geometry Group (VGG16). |
| Databáze: | OpenAIRE |
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