# Automated Diagnosis of Childhood Pneumonia in Chest Radiographs Using Modified Densely Residual Bottleneck-Layer Features

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## Abstract

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## 1. Introduction

#### 1.1. Related Work

#### 1.2. Limitations and Proposed Work

## 2. Proposed Method

#### 2.1. Feature Extraction and Reduction

#### 2.2. Prediction Model

#### 2.3. Adaptive Score Fusion

Algorithm 1: Adaboost ensemble weights learning. |

initialize classifier weights as ${w}_{i}=\frac{1}{No.ofclassifiers}$; |

## 3. Results and Discussion

#### 3.1. SVM Optimization Using Bayesian Optimization

#### 3.2. Normal vs. Bacterial vs. Viral Pneumonia Infected Lungs

#### 3.3. Normal vs. Pneumonia Infected Lungs

#### 3.4. Discussion

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Examples of three CXR images of child human lungs. The left-hand image represents a healthy case, the middle image depicts a viral infection, while a lung with a bacterial infection is shown on the left.

**Figure 2.**The proposed pneumonia diagnosis system depicting feature extraction via residual building blocks, feature reduction, SVM classifier training with Bayesian optimization, and score fusion.

**Figure 3.**The modified connections of residual units showing each block are densely connected to previous blocks. (

**a**) Residual block, (

**b**) dense block, and (

**c**) the modified residual blocks densely connected.

**Figure 4.**Minimum classification error plot of the optimized SVM classifier for the pneumonia diagnosis system using Bayesian optimization vs. random search optimization. (

**a**) Random search optimization, and (

**b**) Bayesian optimization.

**Figure 5.**ROC curves of four sub-classifiers depicting ${S}_{i}\left(\mu \right),{S}_{i}\left(\sigma \right),{S}_{i}(max)$, and ${S}_{i}(min)$ scores compared to the accuracy of the proposed score fusion ${S}_{f}$.

**Figure 6.**Performance of proposed model using bacterial vs. viral pneumonia scenario. (

**a**) is the feature space of bacterial vs. viral infected lung CXR images, and (

**b**) is the SVM with Bayesian optimization of the proposed scenario.

**Figure 7.**The confusion matrices showing a comparison between (

**a**) deep-layer features and (

**b**) features extracted using proposed method.

**Figure 8.**The confusion matrix of the proposed model in a normal vs. bacterial vs. viral CXR images classification fashion using (

**a**) deep features and (

**b**) proposed features.

Category | No. of Images | Training | Testing |
---|---|---|---|

Normal | 1349 | 1012 | 337 |

Bacterial | 2538 | 1903 | 635 |

Viral | 1345 | 1008 | 337 |

Total | 5232 | 3922 | 1309 |

Number of Bottleneck Blocks | Accuracy | Number of Features |
---|---|---|

1 | 95.6 | 1 × 256 |

2 | 96.2 | 1 × 256 |

3 | 96.8 | 1 × 256 |

4 | 96.8 | 1 × 512 |

5 | 99.6 | 1 × 512 |

6 | 97.6 | 1 × 512 |

7 | 97.2 | 1 × 512 |

**Table 3.**Comparison of early-layer and deep-layer accuracy for pneumonia diagnosis using most off-the-shelf deep networks vs. proposed densely-connected residual blocks.

Deep Network | Number of Layers | Accuracy (%) | |
---|---|---|---|

Deep Layer Features | Early Layer Features | ||

AlexNet [5] | 8 | 96.0 | 97.3 |

VGG [17] | 19 | 96.8 | 98.4 |

SqueezeNet [13] | 14 | 96.7 | 96.0 |

GoogleNet [29] | 27 | 96.2 | 97.7 |

ShuffleNet [45] | 20 | 96.5 | 96.8 |

NASNetMobile [46] | 913 | 95.8 | 96.9 |

DenseNet [18] | 201 | 98.0 | 98.4 |

Xception [19] | 36 | 96.4 | 98.4 |

ResNet [6] | 50 | 97.6 | 97.1 |

Proposed method | 35 | na | 99.6 |

Pneumonia Diagnosis Method | Deep Learning Technique | Accuracy (%) |
---|---|---|

Chowdhury et al. [4] | Transfer Learning with SqueezeNet | 99.00 |

Asnaoui et al. [14] | Transfer Learning with ResNet50 | 96.61 |

Saraiva et al. [15] | CNN 10 Layers | 95.30 |

Apostolopoulos et al. [16] | Transfer Learning with MobileNetv2 | 96.78 |

Liang and Zheng [21] | CNN with 49 Residual Blocks | 95.30 |

Kermany et al. [22] | Transfer Learning with AlexNet | 92.80 |

Toğaçar et al. [23] | Deep Features Fused from AlexNet, VGG16, and VGG19 | 99.41 |

Rajpurkar et al. [24] | Transfer Learning with ChexNet | 82.83 |

Han et al. [26] | Contrastive Learning with ResNetAttention | 88.00 |

Chouhan et al. [28] | Transfer Learning with 5 Deep Networks | 96.40 |

Rahman et al. [30] | Transfer Learning with DenseNet201 | 98.00 |

Zhang et al. [31] | One-Class Classification Based Anomaly Detection | 83.61 |

Ayan et al. [32] | Transfer Learning with Ensemble Voting | 95.21 |

Nahiduzzaman et al. [33] | CNN with EML and PCA | 99.83 |

Gour and Jain [34] | fine-tuned EfficientNet-B3 | 99.83 |

Proposed Method | Bottleneck Layer Features with 5 Densely-Connected Residual Building Blocks | 99.60 |

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**MDPI and ACS Style**

Alkassar, S.; Abdullah, M.A.M.; Jebur, B.A.; Abdul-Majeed, G.H.; Wei, B.; Woo, W.L.
Automated Diagnosis of Childhood Pneumonia in Chest Radiographs Using Modified Densely Residual Bottleneck-Layer Features. *Appl. Sci.* **2021**, *11*, 11461.
https://doi.org/10.3390/app112311461

**AMA Style**

Alkassar S, Abdullah MAM, Jebur BA, Abdul-Majeed GH, Wei B, Woo WL.
Automated Diagnosis of Childhood Pneumonia in Chest Radiographs Using Modified Densely Residual Bottleneck-Layer Features. *Applied Sciences*. 2021; 11(23):11461.
https://doi.org/10.3390/app112311461

**Chicago/Turabian Style**

Alkassar, Sinan, Mohammed A. M. Abdullah, Bilal A. Jebur, Ghassan H. Abdul-Majeed, Bo Wei, and Wai Lok Woo.
2021. "Automated Diagnosis of Childhood Pneumonia in Chest Radiographs Using Modified Densely Residual Bottleneck-Layer Features" *Applied Sciences* 11, no. 23: 11461.
https://doi.org/10.3390/app112311461