Journal Article

Comparing the Performance of Accuracy Using 3D CNN Model with the Fixed Spatial Transform With 3D CNN Model for the Detection of Pulmonary Nodules

Ramalingam, Geetha, S, Hemnath

2023

Overview

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.

• Publisher: EDP Sciences
• Subject: 3d cnn model-fixed spatial transform / Accuracy / Algorithms / Artificial neural networks / Confidence intervals / deep learning / diseases / Lung nodules / Neural networks / Nodules / novel 3d cnn / pulmonary nodules / python v.3 programming / Statistical analysis / Three dimensional models