2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 16 , Issue 1 , PP: 52-66, 2024 | Cite this article as | XML | Html | PDF
Sathyamoorthy k. 1 * , Ravikumar S. 2
Doi: https://doi.org/10.54216/FPA.160104
In this work, a statistical model is constructed to forecast the possibility of lung nodules that may grow in the future. This study segments all potential lung nodule candidates using the Multi-scale 3D UNet (M-3D-UNet) method. 34 patients' CT scan series yielded an average of approximately 600 nodule candidates larger than 3 mm, which were then segmented. After removing the arteries, non-nodules and 3D shape variation analysis, 34 actual nodules remained. On actual nodules, the nodule growth Rate (NGR) was calculated in terms of 3D-volume change. Three of the 34 actual nodules had RNG values greater than one, indicating that they were malignant. Compactness, Tissue deficit, Tissue excess, Isotropic Factor and Edge gradient were used to develop the nodule growth predictive measure.
cancer prediction , computed tomography , 3D image segmentation , lung nodule , shape measurement.
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