Early Diagnosis of Ovarian Cancer by the Integration of Whole Side Images and Deep Learning Models

Suma K V, Suma P, Ananya D Hedge, Rakshith R

2025

Abstract

The Ovarian cancer subtypes have been demonstrated to represent unique pathologic entities with varying prognosis and of Ovarian cancers have been shown to be diverse pathologic entities with different treatment outcomes and predictions. Even though pathologists are capable of performing the tissue biopsy process with reliability, there are some challenging situations that necessitate consulting with a specialist. We propose an automated approach for ovarian cancer classification to enhance pathologists' performance and satisfy the need for more accurate and reproducible diagnosis. Whole Slide Images (WSIs) tiled into accessible datasets are used in this study. For the diagnosis and prognosis of ovarian cancer, precise measurement of mitotic activity is essential. In order to identify two forms of mitotic activity, multipolar and caterpillar mitosis that are frequently seen in the histopathology of ovarian tumors, an average of more than 2000 tiles were taken from each of the WSIs using GPU-optimized tiling algorithms. To detect malignant mitotic activity, this paper's focus includes the detection and classification of the aforementioned kinds of mitosis using deep learning architectures. Following training, YOLO-based object detection models achieved accuracies of 78.20% and 89.33%, respectively. A trained ResNet-34 model yielded 86.25%. One important factor that makes it possible for strong deep-learning pipelines for cancer is the tiling technique, which reduces resource usage while preserving good image quality.

Paper Citation

in Harvard Style

K V S., P S., D Hedge A. and R R. (2025). Early Diagnosis of Ovarian Cancer by the Integration of Whole Side Images and Deep Learning Models. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 463-469. DOI: 10.5220/0013621900004664

in Bibtex Style

@conference{incoft25,
author={Suma K V and Suma P and Ananya D Hedge and Rakshith R},
title={Early Diagnosis of Ovarian Cancer by the Integration of Whole Side Images and Deep Learning Models},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={463-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013621900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Early Diagnosis of Ovarian Cancer by the Integration of Whole Side Images and Deep Learning Models
SN - 978-989-758-763-4
AU - K V S.
AU - P S.
AU - D Hedge A.
AU - R R.
PY - 2025
SP - 463
EP - 469
DO - 10.5220/0013621900004664
PB - SciTePress