A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area
Liuyin Chen, Haoyang Qi, Di Lu, Jianxue Zhai, Kaican Cai, Long Wang, Guoyuan Liang, Zijun Zhang
Abstract
We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the execution of the python project based on the dataset used in the original publication or a custom dataset. Detailed steps include data standardization, data preprocessing, model implementation, results display through heatmaps, and statistical analysis process with Origin software or python codes. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022).
Topics & Concepts
Python (programming language)Computer sciencePreprocessorGeneralizability theorySoftwareArtificial intelligenceProtocol (science)Deep learningData miningMachine learningMedicineProgramming languageStatisticsAlternative medicinePathologyMathematicsRadiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentMedical Imaging Techniques and Applications