Childhood Pneumonia Recognition using Convolutional Neural Network from Chest X-ray Images

Diponkor Bala

Abstract


Pneumonia is responsible for around 28% of the deaths of youngsters under five years old in Bangladesh. According to recent analysis, in every 39 seconds one child dies due to pneumonia in all over the world. Pneumonia occurs because of microscopic organisms, infections or growths, and leaves youngsters battling for breath as their lungs load up with discharge and liquid. Delay in looking for suitable consideration and access to different sources for treatment are the fundamental hazard factors for pneumonia demise in small kids in Bangladesh. To fight this problem, this country needed an easily accessible quick solution. With the development of technology, Artificial Intelligence has made human life easier by using various machine learning technique. In last few years, Convolutional Neural Network has becoming an effective way for the classification of multiclass images. This paper works has been developed a system in order to provide a simpler way to detect pneumonia in a short period of time. The model is developed with chest x-ray images taken from the frontal views to identify pneumonia. In this paper, Convolutional Neural Networks (CNN) has been used to recognize pneumonia. The CNN model is trained on a data set of 5,200 recently collected x-ray images. The dataset was divided into two classes, normal and pneumonia x-ray images. We trained our model on different size x-ray images to evaluate its performance. This model can successfully detect pneumonia at an accuracy rate of approximately 98.87%. The proposed CNN model improves the pneumonia recognition accuracy than some existing methods.

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References


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