Classification of Ovarian Cancer Subtype Using Histopathology Images

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Author(s): R. Parvathi (Vellore Institute of Technology, Chennai, India)and Xiaohui Yuan (University of North Texas, India)
Copyright: 2025
Pages: 28
Source title: Signal and Image Processing Techniques for Defense, Security, and Healthcare
Source Author(s)/Editor(s): B. Omkar Lakshmi Jagan (Vignan's Institute of Information Technology, India), Amrit Mukherjee (University of South Bohemia, Czech Republic), Thayyaba Khatoon Mohammed (Malla Reddy University, India)and Vustikayala Sivakumar Reddy (Malla Reddy University, India)
DOI: 10.4018/979-8-3693-3840-7.ch003

Keywords: Computer Science & IT / Computer Vision & Image Processing / Information Science Reference / IT Security and Ethics

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Abstract

Ovarian cancer is a common and reoccurring type of cancer. If detected early and treated properly, one can have a longer lifespan. We aim to provide robust alternative to the pre-existing system of manual diagnosis by automating the process using Deep Learning. In this paper we explore the three methods to classify histopathology images into its five corresponding types and outliers, possible cases of rare subtypes or healthy tissue misdiagnosed as cancerous. Previously, research made about detection and classification of other cancers have yielded significant results with several types of deep learning approaches, however the volume of data and annotations used for those is huge and difficult to obtain for all. Here, to make up for the lack of sufficient data, we have used transfer learning and fine tuning of pre-existing models trained for histopathological data.

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Classification of Ovarian Cancer Subtype Using Histopathology Images

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