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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