Integrating Machine Learning Algorithms for Accurate Ovarian Cancer Diagnosis: A State-of-the-Art Review
P. D. S. S. Lakshmi Kumari, P. Maragathavalli · 2023
Ovarian cancer, a dangerous health problem affecting women everywhere, need for improved diagnostic and prognostic methods. In this research, deep convolutional neural networks are used to focus a thorough exploration of machine learning methods applied to proteomic and imaging datasets. Our research, which included articles published between 2016 and 2023, showed that machine learning algorithms, be able to significantly rise the precision of ovarian cancer identification, prognosis, and treatment response prediction. This review explains how deep CNNs can categorize ovarian cancer tumors effectively in comparison to other approaches. Furthermore, we offer a comparative analysis of several machine learning methods, such as K-Nearest Neighbors, Support Vector Machines, and Artificial Neural Networks, that are utilized to diagnose ovarian cancer. Our results highlight how deeply effective deep learning techniques are at producing superior diagnostic results.