Abstract: Timely and accurate tumor detection in medical imaging is crucial for improving patient outcomes and reducing mortality rates. Traditional methods often rely on manual image interpretation, which is time-intensive and prone to variability. Deep learning, particularly convolutional neural networks (CNNs), has revolutionized tumor detection by automating the process and achieving remarkable accuracy. The present paper investigates the use of YOLOv11, a powerful object detection model, for tumor detection in several medical imaging modalities, such as CT scans, MRIs, and histopathological images. YOLOv11 incorporates architectural advancements, including enhanced feature pyramids and attention processes, allowing accurate identification of tumors with diverse sizes and complexity. The model’s real-time detection capabilities and lightweight architecture render it appropriate for use in clinical settings and resource-limited contexts. Experimental findings indicate that the fine-tuned YOLOv11 attains exceptional accuracy and efficiency, exhibiting an average precision of 91% and a mAP of 68%. This research highlights YOLOv11’s significance as a transformational instrument in the integration of AI in medical imaging, aimed at optimizing diagnostic processes and improving healthcare delivery.

Taoufik Saidani, “Efficient Tumor Detection in Medical Imaging Using Advanced Object Detection Model: A Deep Learning Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01601109

@article{Saidani2025,
title = {Efficient Tumor Detection in Medical Imaging Using Advanced Object Detection Model: A Deep Learning Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01601109},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01601109},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Taoufik Saidani}
}