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In recent times, advancements in image processing have notably enhanced the detection of brain tumors in individuals. Historically, brain tumors were assessed manually using MRI scans, which could lead to human errors. However, with the progress in deep learning techniques, identifying brain tumors and their three variants—meningioma, glioma, and pituitary tumors—has become considerably simpler. There are various deep learning models for object detection like R-CNN, SSD, and R-FCN, that are not able to detect small objects. There are some advance models which has the capability to detect small objects also. Some YOLO that model that detects small objects in real time, which best suits our analysis. In this paper, a comparative analysis of different object detection models like Faster R-CNN, YOLO v5, YOLO v7, and YOLO v11 to carry out to detect brain tumors or segment the portion of the brain tumor. The Brain Tumor IS dataset is used for experimental analysis, it has a total of 801 images, which is divided into 62%, 25%, and 13% with train set, valid set, and test set respectively. Precision, Recall, mAP 50, aAP50-95, and execution time are the key factors used for comparing the models. The experimental result shows that YOLO v7 outperforms other competing models in terms of Precision, mAP 50, and mAP 50-95 except recall and inference speed parameters.

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Brain Tumor Detection and Classification on MRI Images Using YOLO Based Deep Learning Model

  • 会议论文

  • 发表日期：

    2026年

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  • Nitish Kumar,
  • Aman Dhole,
  • Samruddhi Kamble,
  • Gaurav Mishra

摘要

In recent times, advancements in image processing have notably enhanced the detection of brain tumors in individuals. Historically, brain tumors were assessed manually using MRI scans, which could lead to human errors. However, with the progress in deep learning techniques, identifying brain tumors and their three variants—meningioma, glioma, and pituitary tumors—has become considerably simpler. There are various deep learning models for object detection like R-CNN, SSD, and R-FCN, that are not able to detect small objects. There are some advance models which has the capability to detect small objects also. Some YOLO that model that detects small objects in real time, which best suits our analysis. In this paper, a comparative analysis of different object detection models like Faster R-CNN, YOLO v5, YOLO v7, and YOLO v11 to carry out to detect brain tumors or segment the portion of the brain tumor. The Brain Tumor IS dataset is used for experimental analysis, it has a total of 801 images, which is divided into 62%, 25%, and 13% with train set, valid set, and test set respectively. Precision, Recall, mAP 50, aAP50-95, and execution time are the key factors used for comparing the models. The experimental result shows that YOLO v7 outperforms other competing models in terms of Precision, mAP 50, and mAP 50-95 except recall and inference speed parameters.