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Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network | IEEE Conference Publication | IEEE Xplore

Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network


Abstract:

Artificial intelligence has found its use in various fields during the course of its development, especially in recent years with the enormous increase in available data....Show More

Abstract:

Artificial intelligence has found its use in various fields during the course of its development, especially in recent years with the enormous increase in available data. Its main task is to assist making better, faster and more reliable decisions. Artificial intelligence and machine learning are increasingly finding their application in medicine. This is especially true for medical fields that utilize various types of biomedical images and where diagnostic procedures rely on collecting and processing a large number of digital images. The application of machine learning in processing of medical images helps with consistency and boosts accuracy in reporting. This paper describes the use of machine learning algorithms to process chest X-ray images in order to support the decision making process in determining the correct diagnosis. Specifically, the research is focused on the use of deep learning algorithm based on convolutional neural network in order to build a processing model. This model has the task to help with a classification problem that is detecting whether a chest X-ray shows changes consistent with pneumonia or not, and classifying the X-ray images in two groups depending on the detection results.
Date of Conference: 16-20 February 2021
Date Added to IEEE Xplore: 01 April 2021
ISBN Information:
Conference Location: Zabljak, Montenegro

I. Introduction

Artificial intelligence (AI) was recognized as an academic discipline as early as in 1950s, but it was not widely explored by scientific community due to its limited practical feasibility for a long time [1]. In last couple of decades, because of increasing availability of processing power and emergence of big data, AI became a focal point of research and business discussions. AI went through various phases in its development, often regarded as “Seasons of AI”. Until recently the best-known season of AI, was the period from the 1970s to the 2000s and the majority of this period is referred to as the “AI Winter” [2]. One of the main reasons why AI could not develop at a fast pace during this period is the fact that computers did not have sufficient processing power to handle the work required [3]. The comeback of AI started when IBM developed their chess playing program, Deep Blue, which was able to beat world champion Gary Kasparov in 1997 [1]. In the following years AI started to develop rapidly, which resulted in development of new fields such as Machine Learning (ML) and Deep Learning (DL). In contrast to ML, DL uses a set number of traits and requires human input, and can be trained to classify data on its own [2].

References

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