| COVID-19 Detection Based on Chest X-Ray Image Classification using Tailored CNN Model |
| Paper ID : 1043-ICCI2021 (R1) |
| Authors: |
|
Mahmoud Z Fetoh *1, Khalid Amin2, Ahmed Mahmoud Hamad3 1IT, Faculty of computers and information ,Menoufia university, Menoufia, Egypt 2Faculty of Computers and Information Menoufia University Shibin El Kom, Egypt 3Information Technology dep., Faculty of computers and information, Menoufia University, Shebin alkawm, Menoufia, Egypt. |
| Abstract: |
| The outbreak of Covid-19 epidemic led to millions of injuries and deaths and pressure on the health system. The limited availability of diagnosis tools and expert radiologists raise the need of using computer aided tools to diagnosis Covid-19 cases. In this study, a tailored Convolutional Neural Network (CNN) architecture model is proposed to automatically detect Covid-19 cases using chest X-Ray (CXR) images. The proposed CNN model consist of three phases preprocessing, feature extraction and classification. The proposed CNN model depends on kernel separability to reduce the training parameters to a large extent. Furthermore, the proposed model used residual connection and batch normalization extensively to maintain the network stability during the training process and provide the model with the regularization effect in order to reduce the overfitting. Training process hyperparameter (such as batch size and learning rate) are determined dynamically. The proposed architecture is trained using QaTa-Cov19 benchmark dataset achieving 100% for accuracy, sensitivity, precision and F1-score with a very low parameter count (150K) compared with the other methods in the literature. |
| Keywords: |
| Covid-19, CNN, Deep Learning, Residual Connection |
| Status : Paper Accepted |
