New Trends in Ovarian Cancer Diagnosis Using Deep Learning: A Systematic Review
Mohamed El-Khatib, Dan Popescu, Oana Mihaela Teodor, Loretta Ichim
Abstract
Ovarian cancer (OC) is one of the most common types of cancer in women. Surgery and chemotherapy are still the most common forms of treatment; however, their success depends on lots of factors describing the type of cancer, the size, shape, and its origin, and thus early and accurate detection could bring lots of benefits to increasing survival rate by applying custom/ personalized and effective treatment. This is why many researchers aim to obtain accurate computer-aided diagnosis (CAD) systems to assist in the early detection and diagnosis of such diseases. The current paper presents a systematic review of new trends in designing different deep learning-based intelligent systems for accurate OC detection and diagnosis. The paper presents the advantages of using deep learning approaches for OC diagnosis, the most used methods, and datasets. It performs a detailed analysis concerning the most preferred, effective, and accurate architectures. Several 70 articles published in journals and impact conferences were investigated between 2018 and 2024, focusing on 2021 and 2024. All included studies are indexed in PubMed, Scopus, or ISI Web Of Science.