Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.

ABSTRACT: Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy acro

SUBMITTER: Sadeghi MH 

PROVIDER: S-EPMC10867948 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature