Abstract
Pneumonia is one of the most cynical problems to human beings all over the world and detecting the presence of pneumonia in an early stage is very necessary to avoid Premature Death. According to the World Health Organization above 4 million sudden deaths happen each year from domiciliary air pollution correlated diseases inclusive of pneumonia. Generally, pneumonia can be identified using chest X-ray images that are performed by an expert radiologist. But only rely on the radiologist sometimes blocks the treatment because of detecting diseases from the chest X-ray images which requires human effort, experience, and time. In this case, Computer-aided diagnosis (CAD) system is required for identifying pneumonia from chest X-ray images automatically. In this research, a modified model is proposed using Convolution Neural Network (CNN) model to train sample data to relegate and diagnose the presence of pneumonia from an amassment of chest X-ray images. From the experimental result, it is found that the proposed model performs better results (89%) compared to other related existing algorithms.
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Kar, S., Akhtar, N., Rahman, M. (2021). An Approach for Detecting Pneumonia from Chest X-Ray Image Using Convolution Neural Network. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_63
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