Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
---|---|---|
Article Number | 09001 | |
Number of page(s) | 8 | |
Section | Life Science | |
DOI | https://doi.org/10.1051/e3sconf/202339909001 | |
Published online | 12 July 2023 |
Bronchop Neumonia Detection Using Novel Multilevel Deep Neural Network Schema
1 Research Scholar, Bharat Institute of Higher Education and Research, Chennai, India
2 Professor and Head, Lakshmi Narayana Institute of Medical Science, Puduchery, India
3 Associate Professor, School of Business and Management, Christ Deemed to be University, Banglore, India
4 Assistant Professor, ECE Department, PSNA College of Engineering and Technology, Dindigul, India
* Corresponding author: sivanviji@gmail.com
Pneumonia is a dangerous disease that can occur in one or both lungs and is usually caused by a virus, fungus or bacteria. Respiratory syncytial virus (RSV) is the most common cause of pneumonia in children. With the development of pneumonia, it can be divided into four stages: congestion, red liver, gray liver and regression. In our work, we employ the most powerful tools and techniques such as VGG16, an object recognition and classification algorithm that can classify 1000 images in 1000 different groups with 92.7% accuracy. It is one of the popular algorithms designed for image classification and simple to use by means of transfer learning. Transfer learning (TL) is a technique in deep learning that spotlight on pre-learning the neural network and storing the knowledge gained while solving a problem and applying it to new and different information. In our work, the information gained by learning about 1000 different groups on Image Net can be used and strive to identify diseases.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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