Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Autor:	Arockia Xavier Annie R, Aiswarya M S, V S Suryaa
Rok vydání:	2021
Předmět:	General Computer Science Ensemble forecasting Artificial neural network business.industry Computer science Deep learning Pattern recognition medicine.disease Convolutional neural network Ensemble learning Pneumonia Feature (computer vision) X ray image medicine Artificial intelligence business
Zdroj:	International Journal of Advanced Computer Science and Applications. 12
ISSN:	2156-5570 2158-107X
DOI:	10.14569/ijacsa.2021.0121084
Popis:	Pneumonia is a disease caused by a variety of organisms, including bacteria, viruses, and fungi, which could be fatal if timely medical care is not provided. According to the World Health Organization (WHO) report, the most common diagnosis for severe COVID-19 is severe pneumonia. The most common method of detecting Pneumonia is through chest X-ray which is a very time intensive process and requires a skilled expert. The rapid development in the field of deep learning and neural networks in recent years has led to drastic improvement in automation of pneumonia detection from analysing chest x- rays. In this paper, a pre-trained Convolutional Neural Networks (CNN) on chest x-ray images is used as feature extractors which are then further processed to classify the images in order to predict whether a person has pneumonia or not. The different pre- trained Convolutional Neural Networks used are assessed with various parameters regarding their predictions on the images. The results of pre-trained neural networks were examined, and an ensemble model was proposed that combines the predictions of the best pre-trained models to produce better results than individual models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cb2471542c76e1b49c87b869730daae4 https://doi.org/10.14569/ijacsa.2021.0121084 Zobrazit plný text záznamu