Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection.

ABSTRACT: The use of medical images for colon cancer detection is considered an important problem. As the performance of data-driven methods relies heavily on the images generated by a medical method, there is a need to inform research organizations about the effective imaging modalities, when coupled with deep learning (DL), for detecting colon cancer. Unlike previous studies, this study aims to comprehensively report the performance behavior for detecting colon cancer using various imaging modalities coupled with different DL models in the transfer learning (TL) setting to report the best overall imaging modality and DL model for detecting colon cancer. Therefore, we utilized three imaging modalities, namely computed tomography, colonoscopy, and histology, using five DL architectures, including VG

SUBMITTER: Alhazmi W 

PROVIDER: S-EPMC10217759 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature