A New Perspective on the Treatment of Ovarian Cancer: Deep Learning Algorithms-Based Prediction

Yan Yan

2024

Abstract

Ovarian cancer is one of the most common gynaecological cancers worldwide, because the initial symptoms are not obvious, they are often discovered in the late stages, resulting in poor treatment effects and poor prognosis. This paper explores various applications of deep learning (DL) in early detection and personalized treatment of ovarian cancer. The construction process of the deep learning model including data collection, preprocessing, model training and evaluation is introduced in detail. In the application of Convolutional Neural Network (CNN), this paper discusses how to reduce and learn parameters through image enhancement and feature mapping, and how to use SoftMax and cross-entropy loss functions in the later stages of data preprocessing and classification to improve the recognition accuracy of the model. For Long Short Term Memory (LSTM), this paper analyses its significant advantages in handling irregular time series data, especially in handling missing value patterns and complex time dependencies. In addition, this study also explored the clinical challenges of DL models when dealing with ovarian cancer-related issues, such as the “black box” nature of decision-making, generalization capabilities, and privacy issues of sensitive data. The comprehensive results indicate that the DL method will become an effective way to advance the development of the oncology field.

Paper Citation

in Harvard Style

Yan Y. (2024). A New Perspective on the Treatment of Ovarian Cancer: Deep Learning Algorithms-Based Prediction. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 417-421. DOI: 10.5220/0012939300004508

in Bibtex Style

@conference{emiti24,
author={Yan Yan},
title={A New Perspective on the Treatment of Ovarian Cancer: Deep Learning Algorithms-Based Prediction},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={417-421},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012939300004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - A New Perspective on the Treatment of Ovarian Cancer: Deep Learning Algorithms-Based Prediction
SN - 978-989-758-713-9
AU - Yan Y.
PY - 2024
SP - 417
EP - 421
DO - 10.5220/0012939300004508
PB - SciTePress