Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection.
Nahida HabibMd Mahmodul HasanMd Mahfuz RezaMohammad Motiur RahmanPublished in: SN computer science (2020)
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.
Keyphrases
- deep learning
- convolutional neural network
- machine learning
- artificial intelligence
- respiratory failure
- high resolution
- climate change
- neural network
- big data
- community acquired pneumonia
- dual energy
- end stage renal disease
- intensive care unit
- metabolic syndrome
- newly diagnosed
- electronic health record
- dna damage
- oxidative stress
- mass spectrometry
- extracorporeal membrane oxygenation
- young adults
- drug induced
- hepatitis b virus
- magnetic resonance
- liver failure
- reactive oxygen species
- patient reported