GS2-4 Detection of Lung Nodules from CT Image Based on Ensemble Learning

Title:	GS2-4 Detection of Lung Nodules from CT Image Based on Ensemble Learning
Publication:	ICAROB2025
Volume:	30
Pages:	837-840
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2025.GS2-4
Author(s):	Natsuho Baba, Tohru Kamiya, Takashi Terasawa, Takatoshi Aoki
Publication Date:	February 13, 2025
Keywords:	Computer Aided Diagnosis, Machine Learning, Temporal Subtraction Technique, Radiomics, Ensemble Learning
Abstract:	Lung cancer is the most frequently diagnosed cancer worldwide and the leading cause of cancer-related deaths, making early detection and treatment crucial. Temporal subtraction system, one of the CAD, emphasize the differences between the current and previous images. In this study, radiomics features are extracted as explanatory variables from the temporal subtraction images. Feature selection is performed using Elastic Net, followed by the application of machine learning methods. Finally, ensemble learning is applied to classify unknown data into two categories: positive and negative lung nodules.
PDF File:	https://alife-robotics.co.jp/members2025/icarob/data/html/data/GS/GS2/GS2-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/