id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-287145-w518a0wa	Habib, Nahida	Ensemble of CheXNet and VGG-19 Feature Extractor with Random Forest Classifier for Pediatric Pneumonia Detection	2020-10-30		.txt	text/plain	3925	251	52	This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. This paper proposed an ensemble technique of two CNN models-fine-tuned CheXNet and VGG-19 models for the diagnosis of pediatric pneumonia from Chest X-ray images. For the detection and classification of Pneumonia from Normal images different ML algorithms-Random Forest (RF), Adaptive Boosting (AdaBoost), K-Nearest Neighbors (KNN) are applied on the features afterword's. Chest X-ray is easy to use medical imaging and diagnostic technique performed by expert radiologists to diagnose pneumonia, tuberculosis, interstitial lung disease, and early lung cancer [13] . The CheXNet deep CNN model uses this NIH CXR dataset and is said to exceed the average radiologist performance on the pneumonia detection task [8] . The proposed methodology includes image preprocessing using an image enhancement technique and resizing of images, augmentation of training images, finetuning CNN models, model's training, extraction of CNN's feature vector, ensemble of extracted feature vectors, dataset imbalance handling and Pneumonia classification using different machine learning algorithms.	./cache/cord-287145-w518a0wa.txt	./txt/cord-287145-w518a0wa.txt