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@article{172442,
        author = {Yash Bhilare and Sanmay Ethape and Abhijeet Nikam and Aryan Nayak and Prof. Silkesha Thigale},
        title = {Ovavigilance : Empowering Early Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {3407-3411},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172442},
        abstract = {Ovarian cancer is difficult to detect early, leading to late diagnoses and poor survival rates. Traditional methods like imaging and blood tests often fail to identify the disease at an early stage. This research explores the use of machine learning to analyze complex data, such as genetic information and medical images, to detect patterns missed by conventional methods. Machine learning offers a more accurate approach to early detection, which could improve treatment outcomes and increase survival rates for ovarian cancer patients.},
        keywords = {Include relevant terms such as "Ovarian Cancer," "Early Detection," "Machine Learning," "Genetic Data," "Medical Imaging," "Pattern Recognition," "Traditional Diagnostic Methods," and "Survival Rates."},
        month = {January},
        }