A new classification model for covid 19 based on convolutional neural networks
Download as PPTX, PDF0 likes654 views
This document proposes a new convolutional neural network model based on AlexNet to classify CT chest scans into five categories: normal lung, COVID-19, viral pneumonia, bacterial pneumonia, and mycoplasma pneumonia. The model was trained on a dataset of 5000 CT images across the five categories. Experimental results showed the model achieved over 99% accuracy in classifying the different pneumonia types based on CT scans after 9 epochs of training. The authors conclude the proposed model is effective at distinguishing between the five chest CT image types but further optimization may improve performance.
A new classification model for covid 19 based on convolutional neural networks
- 1. A New Classification Model for COVID-19 based on Convolutional Neural Networks and Alexnet Algorithm Walid Hamdy
- 2. Authors Walid Hamdy1, *, Ismail Elansary1, *, Ashraf Darwish 2, * and Aboul Ella Hassanien3, * 1 Faculty1 of Science, Port Said University, Cairo, Egypt 2 Faculty of Science, Helwan University, Cairo, Egypt 3 Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt. *Scientific Research Group in Egypt (SRGE), http://www.egyptscience.net
- 3. Outline Introduction The Proposed Model Results and Conclusion Future work
- 4. Introduction COVID-19 is *: - an infectious disease caused by the recently found virus known as SARS- CoV-2 (or coronavirus). Before the outbreak originated in Wuhan, China on December 2019, there was no information about this virus. Researchers around the world are working to fight against COVID-19 and to reduce its spread and prevent it.
- 5. Introduction Computed tomography (CT) became one of the essential methods for the rapid diagnosis of COVID-19 Deep learning (DL), a branch of Artificial Intelligence, is a family of multi- layer neural network models that excel at the problem of learning from big data Deep learning algorithms are perfect approaches for detecting disease from various types of image sources, such as X-Ray and CT scans. we present a new proposed model based on using AlexNet to classify CT chest scan.
- 6. The Proposed Model We present a new proposed model based on DL using AlexNet Convolutional Neural Network (CNN) to differentiate the abnormal CT chest image regions which refer to different forms of pneumonia. The proposed model distinguish five types of CT chest images: normal lung, COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), and mycoplasma pneumonia (MP).
- 7. The Proposed Model We present a new proposed model based on DL using AlexNet Convolutional Neural Network (CNN) to differentiate the abnormal CT chest image regions which refer to different forms of pneumonia. The proposed model distinguish five types of CT chest images: normal lung, COVID-19, non-COVID-19 viral pneumonia (VP), bacterial pneumonia (BP), and mycoplasma pneumonia (MP).
- 8. Data Set CT chest data set is images CCAP which is a dataset that contains five categories of CT chest images and include about 5000 CT images. BACTERIAL PNEUMONIA (BP) 981 MYCOPLASMA PNEUMONIA (MP) 537 VIRAL PNEUMONIA (VP) 465 COVID-19 1935 NORMAL LUNG 579
- 9. Data Set Sample Normal COVID-19 VIRAL PNEUMONIA Fig1: Data Set Sample MYCOPLASMA PNEUMONIA BACTERIAL PNEUMONIA
- 10. Experimental results: Epoch number AlexNet Epoch 1 98.98 Epoch 2 98.88 Epoch 3 98.06 Epoch 4 99.2 Epoch 5 99.21 Epoch 6 98.72 Epoch 7 98.88 Epoch 8 99.06 Table 1. The results obtained from The proposed model. Epoch 9 99.23
- 11. Experimental results: FIg2. Model Performance.
- 12. Conclusion The proposed model can distinguish five types of CT chest images. The proposed method achieves about 99.2 accuracy. We can use the optimization method to optimize the model.
- 13. Thank You