Abstract
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.
Highlights
Pneumonia is an infection in one or both lungs
World Health Organization (WHO) Child Health Epidemiology Reference Group reported that the median global incidence of clinical pneumonia is 0.28 episodes per child- year
The Convolutional Neural Networks (CNN) were finetuned with the pneumonia chest x-ray dataset which was used for feature extraction
Summary
Pneumonia is an infection in one or both lungs. Bacteria, viruses, and fungi cause it. WHO Child Health Epidemiology Reference Group reported that the median global incidence of clinical pneumonia is 0.28 episodes per child- year. This statistic converts to an annual incidence of 150.7 million new cases, of which 11-20 million (7-13%) are severe enough to require hospital admission [1]. The CNNs were finetuned with the pneumonia chest x-ray dataset which was used for feature extraction. With the advent of technology, it is easier to transfer image files over the phones which could be taken to advantage if the image is of lower resolution as it decreases the file size drastically This helps storing a large database of X-ray images over a device without much hassle. The proposed ensemble model is discussed in the fifth section and it is followed by result analysis and the sixth section concludes
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