IJITEST International Journal of Innovative Trends in Engineering Science and Technology IJITEST 3139-6887 Felix Academic Publications IJITEST-2026-027 10.5281/zenodo.24037943 Original Research Articles Research Article Oral Cancer Detection using Machine Learning D Yeduvaka Aslesha Likitha jackbenison12@gmail.com Priya CH. Swapna swapnachsp@gmail.com A Aishwarya swapnachsp@gmail.com B Anusha Somu swapnachsp@gmail.com C. Safavath Mahalakshmi Bai swapnachsp@gmail.com vignan's Institute of Information and Technology 07 11 2026 1 3 121 125 01 11 2026 07 11 2026 Copyright © 2026 Yeduvaka Aslesha Likitha D, CH. Swapna Priya, Aishwarya A, Anusha Somu B, Safavath Mahalakshmi Bai C. . Published by Felix Academic Publications. 2026 Yeduvaka Aslesha Likitha D, CH. Swapna Priya, Aishwarya A, Anusha Somu B, Safavath Mahalakshmi Bai C. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

Oral cancer is a dangerous health issue that touches thousands of lives annually, particularly in areas with poor access to healthcare centers. Early detection is critical to boosting survival rates, yet numerous individuals lack access to early diagnosis. In this research, we present a smart, light-weight solution through the application of MobileNetV2, a deep learning algorithm with high efficiency and speed. We have developed a simple web application wherein users can input photos of the oral cavity, and the system will immediately tell if there are cancer signs. With the utilization of a pre-trained MobileNetV2 model, our method provides accuracy in addition to running without any issues on low-power devices such as smartphones. The aim is to develop a useful device that can assist physicians and health workers—particularly in remote or underserved communities—and aid in the early detection of oral cancer, when it is most curable.

 MobileNetV2 deep learning oral cancer lightweight solution Image classification pre-trained model healthcare access smartphones medical diagnosis early detection Artificial intelligence. Article Overview

Oral cancer is a dangerous health issue that touches thousands of lives annually, particularly in areas with poor access to healthcare centers. Early detection is critical to boosting survival rates, yet numerous individuals lack access to early diagnosis. In this research, we present a smart, light-weight solution through the application of MobileNetV2, a deep learning algorithm with high efficiency and speed. We have developed a simple web application wherein users can input photos of the oral cavity, and the system will immediately tell if there are cancer signs. With the utilization of a pre-trained MobileNetV2 model, our method provides accuracy in addition to running without any issues on low-power devices such as smartphones. The aim is to develop a useful device that can assist physicians and health workers—particularly in remote or underserved communities—and aid in the early detection of oral cancer, when it is most curable.

 Declarations & Ethics Funding

This research received no external funding or commercial grants.

 Conflict of Interest

The authors declare that there are no competing financial or personal interests related to this work.

 Ethical Approval

Not applicable. This study did not involve human participants, animal subjects, or sensitive clinical data.

 Data Availability

All data supporting the findings of this study are included within the article and its supplementary materials.