# A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features

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## Abstract

**:**

## 1. Introduction

## 2. Material and Methods

#### 2.1. Feature Extraction Using Convolutional Neural Networks

- Chi-square is applied to remove the features which have a high correlation values by computing the dependence between them. It is calculated between each feature for all classes, as in Equation (1):$${\chi}^{2}=\sum _{k=1}^{n}\frac{{({O}_{k}-{E}_{k})}^{2}}{{E}_{k}}$$
- Tree-based classifier is used to calculate feature importance to improve the classification since it has high accuracy, good robustness, and is simple [45].

#### 2.2. Feature Selection Using Artificial Ecosystem-Based Optimization

- Production Procedure: according to the followed procedure in [41], the selection of the producer position is performed in a random way and the corresponding producer is the worst. However, the best solution represented by the decomposer can be modeled as following:$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & {X}_{1}(t+1)=(1-d){X}_{n}\left(t\right)+d\xb7{X}_{rand}\left(t\right),\hfill \end{array}$$$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & d=(1-\frac{t}{{T}_{max}})ran{d}_{1}\hfill \end{array}$$$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & {X}_{rand}\left(t\right)=ran{d}_{2}\left(\right)\xb7(ub-lb)+lb,\hfill \end{array}$$
- Consumption procedure: in such a procedure, the first user feeds to the other user with a lower level of energy or on a producer. Each set of users known as omnivores, vegetarian or herbivores, and carnivores has its mechanism in modernizing its position as follows:
- (a)
- The herbivores locations can be modernized just with respect to the producers:$${X}_{i}(t+1)={X}_{i}\left(t\right)+K\xb7({X}_{i}\left(t\right)-{X}_{1}\left(t\right))$$$$K=\frac{1}{2}\frac{u}{v},\phantom{\rule{2.em}{0ex}}u\in Norm(0,1),v\in Norm(0,1)$$
- (b)
- The update process of the carnivores is performed through the arbitrary customer with several levels of the energy which has an index $\left(l\right)$. Such procedure can be modeled as:$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & {X}_{i}(t+1)={X}_{i}\left(t\right)+K\xb7({X}_{i}\left(t\right)-{X}_{l}\left(t\right)),\hfill \end{array}$$$$\begin{array}{cc}\hfill \phantom{\rule{1.em}{0ex}}& l=randi\left(\right[2\phantom{\rule{0.166667em}{0ex}}i-1\left]\right),\phantom{\rule{0.166667em}{0ex}}i=3,\dots ,N,\hfill \end{array}$$
- (c)
- The position update of omnivores are depends on the producer and as well as the randomly chosen consumer with high level of energy index $\left(l\right)$ as framed follows:$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & {X}_{i}(t+1)={X}_{i}\left(t\right)+K\xb7(ran{d}_{3}\xb7({X}_{i}\left(t\right)-{X}_{1}\left(t\right)))+(1-ran{d}_{3})\xb7({X}_{i}\left(t\right)-{X}_{l}\left(t\right)),\hfill \end{array}$$$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & l=randi\left(\right[2\phantom{\rule{0.166667em}{0ex}}i-1\left]\right),i=3,\dots ,N,\hfill \end{array}$$

- Decomposition process: This represents the last phase in the biological system in which each agent passes on and the remaining parts are separated. This step refers to the exploitation of AEO and it is formulated as in [41]:$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & {X}_{i}(t+1)={X}_{i}\left(t\right)+D\xb7(e\xb7{X}_{n}\left(t\right)-h\xb7{X}_{i}\left(t\right)),i=1,\dots ,N,\hfill \end{array}$$$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & D=3u,\phantom{\rule{1.em}{0ex}}\phantom{\rule{0.166667em}{0ex}}u\in N(0,1)\hfill \end{array}$$$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & e=ran{d}_{4}\xb7randi\left(\left[1\phantom{\rule{1.em}{0ex}}2\right]\right)-1,\hfill \end{array}$$$$\begin{array}{cc}\phantom{\rule{1.em}{0ex}}\hfill & h=2\xb7ran{d}_{4}-1,\hfill \end{array}$$In Equation (7), the parameter D refers to the decomposition factor, h and e represent the weight parameters. $ran{d}_{4}$ is random number generated from [0,1].

Algorithm 1 The AEO algorithm steps [41]. |

Inputs: N the number of solution and ${T}_{max}$: total number of iterations. Generate initial ecosystem X (solutions). Compute the fitness value $Fi{t}_{i}$, and ${X}_{1}$ is the best solution. $t=1$. repeat Update ${X}_{1}$ using Equation (2). ▹ Production for $i=2,\dots ,n$ do ▹ Consumptionif $rand<1/3$ then Update ${X}_{i}$ using Equation (3), ▹ Herbivore else if $1/3<rand<2/3$ then Update ${X}_{i}$ using Equation (6), ▹ Omnivore else Update ${X}_{i}$ using Equation (5), ▹ Carnivore Compute the fitness of each ${X}_{i}$. Find the best solution ${X}_{1}$. ▹ Decomposition Update ${X}_{i}$ using Equation (7). Compute the fitness of each ${X}_{i}$. Update the best solution ${X}_{1}$. $t=t+1$. until ($t<{T}_{max}$) Return ${X}_{1}$. |

## 3. Proposed MobileNet-AEO for Chest X-ray Classification Approach

## 4. Datasets and Evaluation

#### 4.1. Dataset Description

#### 4.2. Evaluation

#### 4.3. Implementation Environment

## 5. Results and Discussion

#### 5.1. Parameters

#### 5.2. Performance

#### 5.3. Comparison with Other CNN Models

#### 5.4. Comparison with Related Works

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 4.**Performance (accuracy, sensitivity and specificity) of models on Shenzhen (

**left**) and Dataset 2 (

**right**).

Dataset 2 | |||||

Shenzhen |

AEO | HHO | HGSO | WOA | SCA | GWO | TLBO | ||
---|---|---|---|---|---|---|---|---|

Acc | Best | 0.9023 | 0.8195 | 0.8120 | 0.8120 | 0.8872 | 0.8421 | 0.8872 |

Mean | 0.8617 | 0.7880 | 0.7835 | 0.7744 | 0.8436 | 0.8150 | 0.8526 | |

Worst | 0.8045 | 0.7368 | 0.7669 | 0.7293 | 0.8271 | 0.7744 | 0.8195 | |

STD | 0.0400 | 0.0374 | 0.0171 | 0.0340 | 0.0252 | 0.0294 | 0.0253 | |

Sens | Best | 0.9194 | 0.8923 | 0.8448 | 0.8636 | 0.8676 | 0.8500 | 0.9077 |

Mean | 0.8839 | 0.8092 | 0.8069 | 0.7848 | 0.8294 | 0.8033 | 0.8369 | |

Worst | 0.8548 | 0.7231 | 0.7759 | 0.7273 | 0.8088 | 0.7667 | 0.7846 | |

STD | 0.0239 | 0.0612 | 0.0256 | 0.0540 | 0.0246 | 0.0361 | 0.0456 | |

Spec | Best | 0.9014 | 0.8382 | 0.8133 | 0.8358 | 0.9385 | 0.8630 | 0.8824 |

Mean | 0.8423 | 0.7676 | 0.7653 | 0.7642 | 0.8585 | 0.8247 | 0.8676 | |

Worst | 0.7465 | 0.6765 | 0.7333 | 0.6567 | 0.8000 | 0.7123 | 0.8529 | |

STD | 0.0609 | 0.0627 | 0.0335 | 0.0711 | 0.0503 | 0.0653 | 0.0104 |

AEO | HHO | HGSO | WOA | SCA | GWO | TLBO | ||
---|---|---|---|---|---|---|---|---|

Acc | Best | 0.9418 | 0.9152 | 0.9041 | 0.9187 | 0.9307 | 0.9349 | 0.9050 |

Mean | 0.9360 | 0.8964 | 0.8955 | 0.8991 | 0.9199 | 0.9322 | 0.8997 | |

Worst | 0.9307 | 0.8767 | 0.8759 | 0.8690 | 0.9110 | 0.9289 | 0.8955 | |

STD | 0.0048 | 0.0153 | 0.0116 | 0.0189 | 0.0081 | 0.0022 | 0.0038 | |

Sens | Best | 0.8722 | 0.8291 | 0.8306 | 0.8384 | 0.8630 | 0.8642 | 0.8148 |

Mean | 0.8518 | 0.7905 | 0.7792 | 0.7848 | 0.8327 | 0.8463 | 0.7872 | |

Worst | 0.8307 | 0.7025 | 0.7264 | 0.6768 | 0.8017 | 0.8210 | 0.7609 | |

STD | 0.0174 | 0.0538 | 0.0393 | 0.0632 | 0.0247 | 0.0183 | 0.0191 | |

Spec | Best | 0.9708 | 0.9495 | 0.9501 | 0.9500 | 0.9612 | 0.9704 | 0.9495 |

Mean | 0.9668 | 0.9357 | 0.9370 | 0.9438 | 0.9561 | 0.9652 | 0.9380 | |

Worst | 0.9637 | 0.9061 | 0.9268 | 0.9310 | 0.9491 | 0.9609 | 0.9288 | |

STD | 0.0027 | 0.0178 | 0.0094 | 0.0075 | 0.0054 | 0.0043 | 0.0091 |

AEO | HHO | HGSO | WOA | SCA | GWO | TLBO | ||
---|---|---|---|---|---|---|---|---|

Dataset1 | Features | 24.6 | 9.6 | 11.6 | 10.8 | 42.2 | 58.8 | 19.6 |

Best time (s) | 8.8713 | 9.1562 | 8.1690 | 4.1322 | 5.1606 | 9.0793 | 9.1075 | |

Mean time (s) | 9.7251 | 9.6065 | 8.5953 | 4.3891 | 5.2591 | 9.5839 | 9.5879 | |

STD time (s) | 0.9068 | 0.3497 | 0.2658 | 0.1653 | 0.0912 | 0.3606 | 0.4349 | |

Dataset2 | Features | 19 | 33.8 | 31.6 | 52.6 | 31.8 | 72 | 30.8 |

Best time (s) | 137.7945 | 250.7114 | 671.3892 | 125.0128 | 251.9918 | 422.1539 | 247.6316 | |

Mean time (s) | 162.4733 | 281.7812 | 743.5204 | 128.3362 | 257.9852 | 467.4882 | 297.0471 | |

STD time (s) | 7.9267 | 30.0768 | 50.8346 | 2.2845 | 9.4944 | 26.7742 | 33.5236 |

**Table 5.**Number of features and performance for Shenzhen dataset and Dataset 2. Best values are shown in bold.

Shenzhen | Features | Percentage | Accuracy | Specificity | Sensitivity |
---|---|---|---|---|---|

MobileNet | 50176 | 100% | 0.89 | 0.89 | 0.90 |

Proposed approach | ∼25 | 0.05% | 0.902 | 0.901 | 0.914 |

Dataset 2 | Features | Percentage | Accuracy | Specificity | Sensitivity |

MobileNet | 50176 | 100% | 0.842 | 0.846 | 0.846 |

Proposed approach | 19 | 0.038% | 0.941 | 0.97 | 0.872 |

**Table 6.**Comparison of results with related works on Shenzhen and Dataset 2. Best values are shown in bold.

Shenzhen Dataset | Feature Extraction | Classifier | Accuracy (%) |
---|---|---|---|

Jaeger et al. [17] | Manually | SVM | 84.10 |

Hwang et al. [21] | Deep features by CNN | KNN | 83.70 |

Lopes et al. [18] | ResNet, VGG and GoogLeNet | SVM | 84.60 |

Sivaramakrishnan et al. [60] | VGG16 with optimal features | CNN | 85.5 |

Proposed approach | Deep features by MobileNet, feature selection by AEO | CNN | 90.2 |

Dataset 2 | Feature extraction | Classifier | Accuracy (%) |

Kermany et al. [48] | Deep features | N/A | 92.8 |

Rajaraman et al. [61] | Deep features by CNN architectures | Residual CNN | 91 |

Inception | 88.6 | ||

Proposed approach | Deep features by MobileNet, feature selection by AEO | CNN | 94.1 |

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**MDPI and ACS Style**

Sahlol, A.T.; Abd Elaziz, M.; Tariq Jamal, A.; Damaševičius, R.; Farouk Hassan, O.
A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features. *Symmetry* **2020**, *12*, 1146.
https://doi.org/10.3390/sym12071146

**AMA Style**

Sahlol AT, Abd Elaziz M, Tariq Jamal A, Damaševičius R, Farouk Hassan O.
A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features. *Symmetry*. 2020; 12(7):1146.
https://doi.org/10.3390/sym12071146

**Chicago/Turabian Style**

Sahlol, Ahmed T., Mohamed Abd Elaziz, Amani Tariq Jamal, Robertas Damaševičius, and Osama Farouk Hassan.
2020. "A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features" *Symmetry* 12, no. 7: 1146.
https://doi.org/10.3390/sym12071146