Enhancing Mobile Acute Lymphoblastic Cancer Detection: Transfer Learning from YOLOv9 to TensorFlow for Real-Time Applications

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Published Jun 26, 2024
https://doi.org/10.56158/jpte.2024.70.3.01
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Vol. 3 No. 01 (2024): June

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Osama Burak Elhalid
Computer engineering Department, Faculty of Engineering and Architecture, Mehmet akif ersoy University, Burdur, Turkey
https://orcid.org/0000-0002-8051-7813
Ali Hakan Işık
Computer engineering Department, Faculty of Engineering and Architecture, Mehmet akif ersoy University, Burdur, Turkey
https://orcid.org/0000-0003-3561-9375

Abstract

The detection of acute lymphoblastic cancer (ALL) is critical for timely diagnosis and treatment. In this study, we propose a novel approach to enhance ALL detection using transfer learning techniques from YOLOv9 to TensorFlow, facilitating real-time application on mobile devices. Leveraging the robustness of YOLOv9 and the versatility of TensorFlow, we fine-tune the pre-trained model to optimize performance for ALL detection. The adapted model is then integrated into a mobile application, enabling users to perform real-time ALL detection using their smartphones. Our results demonstrate the efficacy of the proposed system in accurately identifying ALL cells, with a detection rate of 98% in one scenario and 100% in another. This research represents a significant step forward in leveraging advanced computer vision technologies for mobile healthcare applications, ultimately improving patient outcomes and healthcare accessibility.

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Elhalid, O. B., & Işık, A. H. (2024). Enhancing Mobile Acute Lymphoblastic Cancer Detection: Transfer Learning from YOLOv9 to TensorFlow for Real-Time Applications. International Journal of Pioneering Technology and Engineering, 3(01), 21–26. https://doi.org/10.56158/jpte.2024.70.3.01

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