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@article{187805,
        author = {Nikul Zinzuvadiya and Vivek Baraiya and Nikunj Sidhhapura and Shivani Bhamani and Rutvi Baraiya},
        title = {A Comprehensive Review of Deep Learning and Machine Learning Approaches for Cervical Cancer Detection: Challenges, Advancements, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {7020-7026},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187805},
        abstract = {Cervical cancer remains a leading cause of death among women worldwide, making early detection vital for effective treatment and improved survival rates. This review paper explores recent advancements in artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) models, applied to cervical cancer detection. We examine a variety of state-of-the-art models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models, assessing their efficacy in medical image classification tasks. The challenges of data imbalance, model interpretability, and resource limitations are discussed in depth. Furthermore, we propose future research directions aimed at improving diagnostic accuracy, model generalization, and deployment in low-resource settings. This paper highlights the transformative potential of AI in revolutionizing cervical cancer screening and diagnosis, offering a path toward more accessible and accurate healthcare solutions.},
        keywords = {},
        month = {November},
        }