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.

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