Identification of lung nodules based on combining multi-slice CT images and clinical information
Year: 2025
Type: conference-paper
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.
Authors: Yuto Nishitaki, Tohru Kamiya, Shoji Kido
Institution: Kyushu Institute of Technology
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409914154