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{180493,
        author = {Akash Gawali and Sakshi Kamble and Sakshi Gondhale and Sanika Ghadge and Prof. S. P. Jadhav},
        title = {Oral Cancer Detection using YOLO},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2299-2303},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180493},
        abstract = {order to help medical professionals make an accurate and timely diagnosis, this paper investigates the development and deployment of an AI-powered oral cancer detection system. The system performs real-time object detection on oral images taken with cameras or mobile devices using the YOLO (You Only Look Once) deep learning algorithm. It enables healthcare users to upload images, get instant analysis, and create diagnostic reports when integrated into a web-based platform. Additionally, the system provides effective data management, safe user authentication, and diagnostic performance evaluation tools. The solution, which was created with Python, Flask, and SQLite and implemented on a scalable framework, guarantees usability, accessibility, and dependability. Its potential to improve early detection, lower diagnostic errors, and support healthcare services—particularly in remote or under-resourced areas—is highlighted by preliminary evaluations.},
        keywords = {Oral Cancer Detection, YOLO, Deep Learning, Real-Time Object Detection, Web-Based Platform, Python, Flask, Diagnostic Tool, Healthcare AI.},
        month = {June},
        }