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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 5, 2023.
Abstract: This research paper presents an investigation into the detection of pneumonia using deep learning models and data augmentation techniques. The study compares and evaluates the performance of different models based on experimental results. The proposed model consists of multiple convolutional layers and maxpooling layers. Extensive experiments were conducted on a dataset, and the results demonstrate the efficiency and accuracy of our approach. The findings highlight the potential of deep learning in pneumonia detection and contribute to the existing body of knowledge in this field. The implications of this research can have a significant impact on improving diagnostic accuracy and patient outcomes. Future research directions could explore further enhancements in the model architecture, investigate additional data augmentation techniques, and consider larger datasets for more comprehensive evaluations.
Tawfik Guesmi, “Detecting Pneumonia with a Deep Learning Model and Random Data Augmentation Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 14(5), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01405122
@article{Guesmi2023,
title = {Detecting Pneumonia with a Deep Learning Model and Random Data Augmentation Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01405122},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01405122},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Tawfik Guesmi}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.