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@article{168441,
        author = {Siva Kumar Nagi and Sheri Viharika},
        title = {Enhanced Detection of Brain Tumors in MRI Images Using YOLOv10},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {960-966},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168441},
        abstract = {In this paper, we present a novel approach for detecting brain tumors using the YOLOv10 model, specifically trained to classify two distinct classes: 'Brain Tumor' and 'Eye'. The model was trained and evaluated using a comprehensive dataset of medical images to ensure robust performance across varying conditions. Our experimental results demonstrate that the YOLOv10 model achieved an accuracy of 94.6% for detecting the 'Brain Tumor' class and 92.9% for the 'Eye' class. The overall mean Average Precision (mAP) at an IoU threshold of 0.5 reached 93.7%, indicating the model’s high effectiveness and reliability in identifying and differentiating between brain tumors and eye structures. This research highlights the potential of using advanced object detection models for accurate and efficient medical image analysis, contributing to improved diagnostic accuracy and early intervention strategies.},
        keywords = {Brain Tumor, Eye, Yolov10, object detection, deep learning.},
        month = {October},
        }