| Title: | GS3-3 Identification of lung nodules based on combining multi-slice CT images and clinical information |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 850-853 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.GS3-3 |
| Author(s): | Yuto Nishitaki, Tohru Kamiya, Shoji Kido |
| Publication Date: | February 13, 2025 |
| Keywords: | Computer Aided Diagnosis, Deep Learning, Clinical Information, Multimodal, Multi-slice CT |
| Abstract: | Although chest CT scans are an effective means of diagnosing lung cancer, there are still problems such as the heavy burden on physicians. To solve this problem, computer-aided diagnosis (CAD) systems are being introduced. Conventional CAD systems are based on a method that uses only image information. In this study, we propose a method for identifying nodular shadows that integrates a composite image created from multi-slice CT images and clinical information such as the patient's age, sex, and medical history in the medical record. The proposed method extracts features from the multi-section CT and clinical information, respectively, integrates the features, and then performs binary classification of nodules and vessels using a classifier. The proposed method achieved a very high accuracy of Accuracy=0.983, TPR=0.987, and FPR=0.018. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/GS/GS3/GS3-3.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/ |
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