Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Abstract

Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

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

“…One of the challenges of DL in ovarian cancer diagnosis is the need for large amounts of high-quality data to train the models effectively. The quality of the data is essential to ensure that the models can generalize to new cases accurately [ 69 ]. Utilizing a small dataset during the training phase can result in overfitting [ 6 ].…”

“…DL has shown great potential in analysing genomic data for cancer diagnosis and prognosis [ 70 ]. Additionally, DL can be used to develop personalized diagnostic approaches that take into account individual patient characteristics [ 69 ].…”

“…Nevertheless, there are still important research directions to pursue in the field of DL for ovarian cancer diagnosis. Cost-effectiveness analyses should be conducted to evaluate the economic impact of implementing DL techniques in routine clinical practice for ovarian cancer diagnosis [ 50 , 69 ]. By addressing these research areas, further advancements can be made in leveraging DL techniques to enhance the accuracy, efficiency, and reliability of ovarian cancer diagnosis.…”