Combining CT Images and Clinical Features of Four Periods to Predict Whether Patients Have Rectal Cancer

In this paper, we mainly use random forest and broad learning system (BLS) to predict rectal cancer. A total of 246 participants with computed tomography (CT) image records were enrolled. The total model in the training set (combined with imaging and clinical indicators) has the best prediction resu...

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Detalles Bibliográficos
Autores principales: Feng, Yingyin, Ding, Qi, Meng, Chen, Wang, Wenfeng, Zhang, Jingjing, Lian, Huixiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670968/
https://www.ncbi.nlm.nih.gov/pubmed/34917137
http://dx.doi.org/10.1155/2021/4662061

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670968/
https://www.ncbi.nlm.nih.gov/pubmed/34917137
http://dx.doi.org/10.1155/2021/4662061

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