Pneumonia Detection on Chest X-ray Using Deep Convolutional Neural Networks | IEEE Conference Publication | IEEE Xplore

Pneumonia Detection on Chest X-ray Using Deep Convolutional Neural Networks


Abstract:

The manual interpretation of medical images in healthcare facilities often delays patient diagnosis. This process is characterized by slow, time-consuming, costly, and la...Show More

Abstract:

The manual interpretation of medical images in healthcare facilities often delays patient diagnosis. This process is characterized by slow, time-consuming, costly, and labor-intensive procedures. Pneumonia, a severe respiratory infection affecting the lungs, poses a significant threat, necessitating prompt detection and treatment to prevent fatalities. This study introduces an automated method utilizing deep learning to diagnose pneumonia. An approach employing a simplified CNN architecture has been devised, requiring less computational power. This approach can be particularly beneficial for healthcare facilities with limited resources. Experimentation conducted on a public image dataset yielded a model accuracy of 0.95. The study's findings demonstrate the proposed technique's efficacy, surpassing existing CNN models' accuracy and computational efficiency. This paper underscores the significance of deep learning in diagnosing and ensuring timely treatment for pneumonia.
Date of Conference: 25-27 July 2024
Date Added to IEEE Xplore: 08 October 2024
ISBN Information:
Conference Location: Sydney, Australia

I. Introduction

Medical imaging has revolutionized the field of healthcare by providing invaluable insights into the human body's internal structures and functions [1]. Accurate and timely interpretation of medical images is essential for diagnosis and treatment planning. In recent years, deep learning, a subset of artificial intelligence, has sparked a transformative shift in how medical imagery is analyzed and interpreted [2]. This shift has enhanced the efficiency of medical image interpretation and has shown great promise in improving diagnostic accuracy, particularly in diseases like pneumonia.

References

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