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Classification Of Ovarian Tumor Using Histopathological Images

Tikone, Minakshi Goraksh (2022) Classification Of Ovarian Tumor Using Histopathological Images. Masters thesis, Dublin, National College of Ireland.

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Histopathology and allied sciences such as cytology have gained new perspectives as a result of the rise of digital pathology. Computer algorithms, specifically artificial-intelligence algorithms, can work on digitized slides to aid pathologists with theranostic and diagnostic duties. In recent years, machine learning has come a long way. Machine learning approaches such as segmentation and classifications have apparent advantage of doing pathological image analysis. In this research, I provide the findings of a search of the world’s biggest public archive ([TCGA]The Cancer Genome Atlas program run by the National Cancer Institute in the United States) of entire slide pictures of over 50 individuals with ovarian tumor cells. I successfully indexed and searched around high-resolution digital slides totaling 8 giga bytes of data composed of 8000 x 8500 pixels picture patches for the first time. This study demonstrate a pretained DCNN architecture -VGG16 which consist of 16 layers. The model was trained with 46 images, achieved an accuracy score of 44% due to the limitation issue. The proposed study is to use VGG16 model to classify the ovarian tumor using histopathological images.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Histopathology; machine learning; DCNN; pre-trained model; VGG16
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
R Medicine > RB Pathology
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 14 Mar 2023 10:01
Last Modified: 14 Mar 2023 10:01

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