# Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features

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## Abstract

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## 1. Introduction

## 2. Recent Studies

## 3. Deep Convolutional Neural Networks

## 4. Experimental Dataset

## 5. Method

#### 5.1. Deep Learning Features

_{0}is the bias, and f is the nonlinear function. Basically, on the basis of the input, the ANN is trained to predict the output using the various neurons within the system. The main requirement is the quantity of the input size that allows for the system to capture features of the input to allow accurate prediction. The ANN can have multiple layers, with each consisting of perceptrons, which are referred to as hidden layers.

#### 5.1.1. GoogleNet

#### 5.1.2. ResNet18

#### 5.2. Classification

#### 5.2.1. Fast Decision Tree (FDT)

^{2}), where m is the number of training datasets, and n is the number of attributes. This algorithm outperforms the Bayesian algorithm on larger datasets, while the Bayesian algorithm behaves better on smaller datasets. There are two approaches to the fast decision tree algorithm, namely, a restricted model space search and a powerful search heuristic [45]. Figure 4 shows part of an FDT with a pattern ${\mathrm{x}}_{\mathrm{i}}=\left({\mathrm{x}}_{\mathrm{i}1},{\mathrm{x}}_{\mathrm{i}2},\dots ,{\mathrm{x}}_{\mathrm{in}}\right)$, n-dimensional space, and node t

_{i}. For each threshold t

_{i}, a decision is made for a pattern x

_{i}to identify its class. The process continues until the training dataset is exhausted. For example, if x

_{1}> a1, where a1 is a threshold, then x

_{1}belongs to class C

_{0}, and the process keeps going.

#### 5.2.2. Random Forest (RF)

#### 5.2.3. Support Vector Machine (SVM)

_{diff}represents the distance between the positive and negative hyperplane, which is called the margin. The maximation of this margin can be reached by maximizing the value $\frac{2}{\Vert w\Vert}$. Figure 6 shows the positive and negative hyperplanes with the maximum margin.

#### 5.2.4. Bayesian Network (BN)

## 6. Results

## 7. Discussion

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 9.**Comparison of the confusion matrix for different classifiers with different deep learning-based feature sets.

Class | Total of Patients | Total Images | Sample CT Scan Images | ||
---|---|---|---|---|---|

CP | 932 | 35,191 | |||

NCP | 929 | 21,872 | |||

Normal | 850 | 28,548 |

Method | Accuracy | Precision | Recall | F1-Measure | Training Time |
---|---|---|---|---|---|

AlexNet | 98.49 | 0.986 | 0.977 | 0.982 | 25,568 |

VGG16 | 97.32 | 0.971 | 0.960 | 0.972 | 73,756 |

GoogleNet | 98.71 | 0.984 | 0.972 | 0.978 | 54,866 |

Method | Accuracy | Precision | Recall | F1-Measure | Training Time |
---|---|---|---|---|---|

Random forest | 93.25 | 0.932 | 0.897 | 0.979 | 162 |

Support vector machine | 99.61 | 0.996 | 0.994 | 0.997 | 6027 |

Fast decision tree | 93.25 | 0.932 | 0.897 | 0.979 | 162 |

Bayesian network | 81.49 | 0.81 | 0.729 | 0.937 | 178 |

Method | Accuracy | Precision | Recall | F1-Measure | Training Time |
---|---|---|---|---|---|

Random forest | 97.78 | 0.978 | 0.967 | 0.999 | 202 |

Support vector machine | 99.86 | 0.999 | 0.998 | 0.999 | 7513 |

Fast decision tree | 93.47 | 0.999 | 0.998 | 0.999 | 133 |

Bayesian network | 80.14 | 0.798 | 0.708 | 0.93 | 210 |

**Table 5.**Experimental results using the combined GoogleNet and ResNet18 features with different classifiers.

Method | Accuracy | Precision | Recall | F1-Measure | Training Time |
---|---|---|---|---|---|

Random forest | 97.93 | 0.979 | 0.969 | 0.999 | 241 |

Support vector machine | 99.90 | 0.999 | 0.998 | 0.999 | 12,382 |

Fast decision tree | 94.45 | 0.944 | 0.915 | 0.984 | 302 |

Bayesian network | 81.88 | 0.814 | 0.736 | 0.931 | 404 |

Reference | Method | Data | Accuracy |
---|---|---|---|

Proposed method | Hybrid ResNet18 and GoogleNet 2000 features with SVM | CC-CCII dataset | 99.91% |

Kang et al. (2020) [49] | A custom-designed 7-layered 3D CNN model | CC-CCII dataset | 88.94% |

Xing et al. (2020) [50] | Hybrid active learning with 2D U-Net and residual network | CC-CCII dataset | 95% |

Li et al. (2021) [51] | Hybrid generative adversarial network and DenseNet | CC-CCII dataset | 85% |

Fu et al. (2021) [52] | Densely connected attention network (DenseNet) | CC-CCII dataset | 96.06% |

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**MDPI and ACS Style**

Latif, G.; Morsy, H.; Hassan, A.; Alghazo, J.
Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features. *Viruses* **2022**, *14*, 1667.
https://doi.org/10.3390/v14081667

**AMA Style**

Latif G, Morsy H, Hassan A, Alghazo J.
Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features. *Viruses*. 2022; 14(8):1667.
https://doi.org/10.3390/v14081667

**Chicago/Turabian Style**

Latif, Ghazanfar, Hamdy Morsy, Asmaa Hassan, and Jaafar Alghazo.
2022. "Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features" *Viruses* 14, no. 8: 1667.
https://doi.org/10.3390/v14081667