Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183128,
        author = {A Mary},
        title = {A REVIEW ON INTEGRATION OF MACHINE LEARNING FOR THE EARLY DETECTION OF CANCER THROUGH IMAGE ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {192-196},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183128},
        abstract = {The early identification of cancer is essential for enhancing patient outcomes and increasing survival rates. Conventional diagnostic techniques frequently encounter difficulties in accurately detecting early-stage cancers, which can result in postponed treatment and diminished opportunities for effective intervention. Recent advancements in artificial intelligence, particularly in machine learning and deep learning, have greatly improved the ability to diagnose and forecast cancer. This review examines the application of multi-modal imaging data, genomics, and clinical parameters to implement machine learning strategies in the early diagnosis of cancer. The integration of machine learning with imaging data obtained from various modalities has been shown to be an effective approach for enhancing the diagnostic precision of early cancer detection. This review will explore the current landscape of machine learning in the diagnosis of early-stage cancer, with a focus on the analysis of multi-modal imaging.},
        keywords = {Machine learning; Deep learning; Multi-modal imaging; Early cancer detection; Early diagnosis; Imaging analysis; Artificial intelligence},
        month = {July},
        }