International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 June 2025
p-ISSN: 2395-0072
www.irjet.net
Optimal Hybrid Model for Early Diagnosis of Ovarian Cancer Considering Clinical Biomarkers and Imaging Features. Mr. Vishweshwar Nath Pandey1, Dr. S. Vairachilai2 1 Mr. Vishweshwar Nath Pandey Department of Computer Science and Engineering
PhD (CSA) Scholar, Sanskriti University Mathura, U.P. India vnpoffice04@gmail.com
2Dr. S. Vairachilai Department of Computer Science and Engineering Professor Dean
& Dean, Sanskriti University, Mathura, U.P. India
-----------------------------------------------------------------------***--------------------------------------------------------------------Abstract – Ovarian cancer is one of the leading causes of cancer-related deaths among women due to its late diagnosis and lack of effective early screening methods. This study focuses on developing an accurate and reliable system for early detection of ovarian cancer by combining ensemble machine learning and deep learning techniques. Using ultrasound imaging data along with biomarker information, the proposed model aims to improve diagnostic accuracy and reduce false positives. The integration of multiple data sources and advanced algorithms enhances the ability to identify cancer at an early stage, potentially increasing survival rates and treatment effectiveness. The results demonstrate the effectiveness of the proposed approach, offering a promising tool for clinical use in ovarian cancer screening.
ultrasound imaging and biomarker information to detect ovarian cancer earlier and more reliably.
This approach could lead to better screening tools and improved outcomes for patients. 1.2 Problem Statement and Research Question The following important questions are addressed in this study: 1. Early detection of ovarian cancer is challenging due to vague symptoms and limited accuracy of current screening methods like ultrasound and biomarker tests. 2. Late diagnosis leads to poor treatment outcomes and high mortality rates, highlighting the need for better detection techniques. 3. imaging and biomarker data with ensemble machine learning and deep learning models can improve early and accurate detection of ovarian cancer.
Keywords: Ovarian cancer, early detection, ensemble machine learning, deep learning, ultrasound imaging, biomarkers, cancer diagnosis, medical imaging, predictive modeling, cancer screening.
1. Introduction
1.3 Significance of the Study
1.1 Background and Context of the Research Topic
Ovarian cancer is one of the deadliest gynecological cancers due to its late diagnosis and rapid progression. Early detection significantly improves survival rates and treatment effectiveness. However, current diagnostic methods often fail to identify the disease at an early stage because of nonspecific symptoms and limitations in conventional imaging and biomarker analysis. This study is significant as it aims to develop a more reliable and accurate detection system by integrating ultrasound imaging with biomarker data using advanced ensemble machine learning and deep learning techniques. The proposed approach has the potential to overcome the weaknesses of individual methods, providing a comprehensive and precise diagnosis tool. This research can contribute to earlier intervention, better patient outcomes, and reduced healthcare costs associated with late-stage treatment. Moreover, the findings may guide future development of non-invasive, cost-effective screening strategies, thereby having a meaningful impact on clinical practice and improving the quality of life for women at risk of ovarian cancer.
Ovarian cancer is one of the leading causes of cancerrelated deaths among women worldwide. It is often called a “silent killer” because early symptoms are unclear and easy to miss. This leads to most cases being diagnosed at an advanced stage, making treatment less effective and survival rates low. Early detection is essential to improve the chances of successful treatment and patient survival. Traditional screening methods, such as pelvic exams and blood tests, often fail to detect ovarian cancer early. Medical imaging, especially ultrasound, is widely used to assess ovarian tumors, but interpreting these images accurately can be challenging. In recent years, machine learning and deep learning techniques have shown great promise in improving the accuracy of cancer diagnosis by analyzing medical images and biomarker data. This research aims to use ensemble machine learning and deep learning methods to combine
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