A Review of Deep Learning Models for Early Detection and Diagnosis of Ovarian Cancer

Savitha D; Rajakumari D

doi:10.54392/irjmt2519

Authors

Savitha D Department of Computer Science, Nandha Arts and Science College, Erode, Tamil Nadu, India. Author https://orcid.org/0009-0008-9625-3884
Rajakumari D Department of Computer Science, Nandha Arts and Science College, Erode, Tamil Nadu, India. Author

DOI:

https://doi.org/10.54392/irjmt2519

Keywords:

Healthcare Analysis, Ovarian Cancer Diagnosis, Machine Learning, Deep Learning, Early Detection

Abstract

Ovarian cancer ranks seventh worldwide and is the third most common type of cancer diagnosed in women in India. Numerous studies have demonstrated that the number of people affected by ovarian cancer is expected to rise significantly in the future. Proactive measures for early cancer detection are essential to prevent death and recurrence. This paper attempts to review the various deep learning (DL) models in ovarian cancer diagnosis, including detecting risk factors, analyzing genomic data sets, predicting disease progression, recurrence, and mortality rates, and identifying correlations and patterns. The patient's electronic health records contain effective analytics on imaging and other types of data that may open the door to more accurate or early identification of ovarian cancer. The taxonomy of the several ways that DL aids in the diagnosis, early detection, and treatment of ovarian cancer will be compiled in this review article. As per the reviews, more research studies have examined the Convolutional Neural Networks (CNNs) approach for the Early Detection and Diagnosis of Ovarian Cancer. This is because CNNs are a popular and potent architecture for image classification tasks because of their capacity to learn spatial and hierarchical features from images effectively. The review article seeks to give future research topics and assess the state-of-the-art application of DL algorithms for ovarian cancer diagnosis.

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