# New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Convolutional Neural Network (CNN)

#### 2.1.1. Convolutional Layers

#### 2.1.2. Pooling Layer

#### 2.1.3. Fully Connected (FC) Layer

#### 2.2. Classifiers

#### 2.2.1. Softmax Classifier

#### 2.2.2. Support Vector Machines (SVM)

#### 2.2.3. K-Nearest Neighbors (KNN)

#### 2.2.4. Naïve Bayes (NB)

#### 2.2.5. Decision Trees (DT)

#### 2.3. Ant Lion Optimization (ALO) Algorithm

#### 2.4. Proposed Method

#### 2.4.1. Data Resizing and Feature Extraction

#### 2.4.2. Feature Selection

- Decrease Overfitting: Less redundant features mean less chance to encounter decisions based on noise.
- Enhance Accuracy: Less misleading features mean an increase in model accuracy.
- Decrease Training Time: Less features means that the classifiers train faster.

#### 2.4.3. Classifiers

## 3. Results

## 4. Discussion

- Random choice of antlions and the usage of a roulette wheel ensure exploration of the search space.
- Random walks of ants around the antlions additionally accentuate exploration of the search range around the antlions.
- The local optima are resolved by using roulette wheel support and random walk.
- ALO approximates the global optima by avoiding the local optima in the population of search agents.
- ALO algorithm is flexible and appropriate for solving various problems, as it has small number of adaptive parameters to fine-tune.
- PSO is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. This causes problems for feature selection, especially from complex data such as COVID-19 X-ray images.
- GA is computationally expensive. Consequently, GA implementation requires high amount of optimization. Moreover, designing an objective function and acquiring the representation and operators right can be difficult.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**DT Tree Induction Using Information Theory [40].

**Figure 5.**Confusion Matrix [52].

Classes | Dataset from [53] | Dataset from [54] |
---|---|---|

COVID-19 | 3616 | 576 |

Lung Opacity | 6012 | - |

Normal | 10,200 | 1583 |

Pneumonia | 1345 | 4273 |

**Table 2.**Experimental Results of CNN + Classifiers. Data from [53].

Classifiers | Accuracy | Precision | F1 Score | Sensitivity | Specificity |
---|---|---|---|---|---|

NB | 0.9636 | 0.9200 | 0.9583 | 0.9408 | 0.9310 |

SVM | 0.9455 | 0.9200 | 0.9388 | 0.9377 | 0.9406 |

Soft Max | 0.9091 | 0.9200 | 0.9020 | 0.9190 | 0.9019 |

KNN | 0.8909 | 0.8800 | 0.8800 | 0.8703 | 0.8954 |

DT | 0.8448 | 0.8148 | 0.8300 | 0.8033 | 0.8258 |

**Table 3.**Experimental Results of CNN + ALO + Classifiers. Data from [53].

Classifiers | Accuracy | Precision | F1 Score | Sensitivity | Specificity |
---|---|---|---|---|---|

NB | 0.9801 | 99.95 | 0.9804 | 0.9823 | 0.9856 |

SVM | 0.9605 | 0.9550 | 0.9600 | 0.9605 | 0.9566 |

Soft Max | 0.9173 | 0.9540 | 0.9231 | 0.9211 | 0.9412 |

KNN | 0.9355 | 0.8700 | 0.9362 | 0.8907 | 0.9011 |

DT | 0.8627 | 0.8600 | 0.8627 | 0.8720 | 0.8814 |

**Table 4.**Experimental Results of CNN + Classifiers. Data from [54].

Classifiers | Accuracy | Precision | F1 Score | Sensitivity | Specificity |
---|---|---|---|---|---|

NB | 0.9776 | 0.9467 | 0.9656 | 0.9576 | 0.9409 |

SVM | 0.9609 | 0.9398 | 0.9456 | 0.9569 | 0.9534 |

Soft Max | 0.9378 | 0.9200 | 0.9020 | 0.9190 | 0.9019 |

KNN | 0.9065 | 0.8901 | 0.8709 | 0.8809 | 0.8954 |

DT | 0.8542 | 0.8148 | 0.8300 | 0.8033 | 0.8258 |

**Table 5.**Experimental Results of CNN + ALO + Classifiers. Data from [54].

Classifiers | Accuracy | Precision | F1 Score | Sensitivity | Specificity |
---|---|---|---|---|---|

NB | 0.9801 | 0.9787 | 0.9745 | 0.9604 | 0.9594 |

SVM | 0.9767 | 0.9567 | 0.9698 | 0.9677 | 0.9645 |

Soft Max | 0.9498 | 0.9309 | 0.9295 | 0.9245 | 0.9324 |

KNN | 0.9065 | 0.8993 | 0.8795 | 0.8886 | 0.8975 |

DT | 0.8542 | 0.8175 | 0.8397 | 0.8095 | 0.8284 |

**Table 6.**Accuracy comparison of significant works with the proposed CNN + ALO + NB method. Data from [54].

Ref | Method | Accuracy (%) |
---|---|---|

[13] | Bayes-SqueezeNet | 98.83 |

[63] | Tailored CNN | 92.30 |

[64] | DenseNet | 88.90 |

[65] | Capsule Networks | 95.70 |

[66] | ResNet50 | 96.20 |

[67] | Sgdm-SqueezeNet | 98.30 |

[68] | DarkNet-19 based CNN | 87.02 |

[69] | Transfer learning with Xception | 96.60 |

[70] | Transfer learning with MobileNetV2 | 96.80 |

[71] | CoroDet | 94.2 |

[72] | COVINet | 97 |

[73] | Shallow CNN | 95 |

[74] | CovXNet | 97.6 |

Proposed Method | CNN + ALO + NB | 99.63 |

**Table 7.**Accuracy comparison of significant works with the proposed CNN+ALO+NB method. Data from [53].

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**MDPI and ACS Style**

Karim, A.M.; Kaya, H.; Alcan, V.; Sen, B.; Hadimlioglu, I.A. New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images. *Symmetry* **2022**, *14*, 1003.
https://doi.org/10.3390/sym14051003

**AMA Style**

Karim AM, Kaya H, Alcan V, Sen B, Hadimlioglu IA. New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images. *Symmetry*. 2022; 14(5):1003.
https://doi.org/10.3390/sym14051003

**Chicago/Turabian Style**

Karim, Ahmad Mozaffer, Hilal Kaya, Veysel Alcan, Baha Sen, and Ismail Alihan Hadimlioglu. 2022. "New Optimized Deep Learning Application for COVID-19 Detection in Chest X-ray Images" *Symmetry* 14, no. 5: 1003.
https://doi.org/10.3390/sym14051003