Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
---|---|---|
Article Number | 09003 | |
Number of page(s) | 10 | |
Section | Life Science | |
DOI | https://doi.org/10.1051/e3sconf/202339909003 | |
Published online | 12 July 2023 |
Comparing the Performance of Accuracy Using 3D CNN Model with the Fixed Spatial Transform With 3D CNN Model for the Detection of Pulmonary Nodules
1 Research Scholar, Department of Medical Instrumentation, Saveetha School of Engineering, saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu, India
2 Project guide, Department of Medical Instrumentation, Saveetha School of Engineering, saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu, India
* Corresponding author: hemanthsaveetha@gmail.com
Aim: The research study aims to detect the accuracy level of the pulmonary nodule using a convolutional neural network (CNN). The comparison between the Novel 3D CNN-fixed spatial transform algorithm and Novel 3D CNN Model algorithm for accurate detection. Materials and Methods: The information for this study was gained from the Kaggle website. The samples were taken into consideration as (N=20) for 3D CNN-fixed spatial transform and (N=20) 3D CNN Model according to the clinical. com, total sample size calculation was performed. Python software is used for accurate detection. Threshold Alpha is 0.05 %, G power is 80% and the enrollment ratio is set to 1. Result: This research study found that the 3D CNN with 89.29% of accuracy is preferred over 3D CNN with fixed spatial transform which gives 78.5% accuracy with a significance value (p=0.001), (p<0.05) with a 95% confidence interval. There is statistical significance between the two groups. Conclusion: The mean value of 3D CNN -fixed spatial transform is 78.5% and Novel 3D CNN is 89.29%.Novel 3D CNN appears to give better accuracy than 3D CNN-fixed spatial transform.
Key words: Pulmonary Nodules / Novel 3D CNN / Diseases / 3D CNN Model-Fixed Spatial Transform / Deep Learning / Python V.3 Programming
© The Authors, published by EDP Sciences, 2023
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