| Autor: |
Jayesh Sharma, Akshit Panchal, Ayush Gautam, Ankit Vijayvargiya, Abhijeet Parashar, Rajesh Kumar |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). |
| DOI: |
10.1109/icirca51532.2021.9544791 |
| Popis: |
The COVID-19 (previously known as “2019 novel coronavirus”) took the big form and outspread rapidly around the world becoming a pandemic. Artificial intelligence tools come out to be one of the fastest solutions to detect the disease and in another way helping to control the spread. This paper signifies how chest X-ray images use deep learning techniques which are very useful for analyzing images to detect the virus and spotting high-risk patients for controlling the spread. Further, it shows how the Convolutional Neural Network (CNN) technology of deep learning helps to detect the virus quickly. A CNN is a type of artificial neural network that is used for image pre-processing and consists of many layers that aid in detection. A sequential CNN model is proposed with different kernel sizes, filters, and having different parameters using a dataset of 2159 images. The output shows that a model with an adequate amount of filters, max-pooling layers, dropout layers and dense layers imparts the highest accuracy of 99.53% in detecting the coronavirus. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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