Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection

Abstract

Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Convolutional Neural Networks (CNNs)-CheXNet and VGG-19 are trained and used to extract features from given X-ray images. These features are then ensembled for classification. To overcome data irregularity problem, Random Under Sampler (RUS), Random Over Sampler (ROS) and Synthetic Minority Oversampling Technique (SMOTE) are applied on the ensembled feature vector. The ensembled feature vector is then classified using several Machine Learning (ML) classification techniques (Random Forest, Adaptive Boosting, K-Nearest Neighbors). Among these methods, Random Forest got better performance metrics than others on the available standard dataset. Comparison with existing methods shows that the proposed method attains improved classification accuracy, AUC values and outperforms all other models providing 98.93% accurate prediction. The model also exhibits potential generalization capacity when tested on different dataset. Outcomes of this study can be great to use for pneumonia diagnosis from chest X-ray images.

Keywords: AUC; Convolutional neural network; Ensemble method; Machine learning; Pneumonia; SMOTE.

Fig. 1

Methodological steps of pneumonia detection

Fig. 2

Architectural design of fine-tuned CheXNet Model

Fig. 3

Fine-tuned VGG-19 architecture

Fig. 4

Random forest classifier

Fig. 5

Preprocessed normal and pneumonia image of dataset [9]

Fig. 6

Preprocessed normal and pneumonia image of dataset [21]

Fig. 7

a CheXNet performance on validation dataset. b VGG-19 performance on validation dataset

Fig. 8

Proposed final model’s performance on validation dataset

Fig. 9

Proposed final model’s performance on new test dataset

Fig. 10

ROC curve of proposed final model