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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Neurotechnology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1445882
This article is part of the Research Topic Computer Vision and Image Synthesis for Neurological Applications View all articles

Enhancing Brain Tumor Detection in MRI Images using YOLO-NeuroBoost Model

Provisionally accepted
Aruna Chen Aruna Chen 1Da Lin Da Lin 2*Qiqi Gao Qiqi Gao 1
  • 1 Inner Mongolia Normal University, Hohhot, Inner Mongolia, China
  • 2 Inner Mongolia University, Hohhot, China

The final, formatted version of the article will be published soon.

    Brain tumors are diseases characterized by abnormal cell growth within or around brain tissues, including various types such as benign and malignant tumors. However, there is currently a lack of early detection and precise localization of brain tumors in MRI images, posing challenges to diagnosis and treatment. In this context, achieving accurate target detection of brain tumors in MRI images becomes particularly important as it can improve the timeliness of diagnosis and the effectiveness of treatment. To address this challenge, we propose a novel approach-the YOLO-NeuroBoost model. This model combines the improved YOLOv8 algorithm with several innovative techniques, including dynamic convolution KernelWarehouse, attention mechanism CBAM (Convolutional Block Attention Module), and Inner-GIoU loss function. Our experimental results demonstrate that our method achieves mAP scores of 99.48 and 97.71 on the Br35H dataset and the open-source Roboflow dataset, respectively, indicating the high accuracy and efficiency of this method in detecting brain tumors in MRI images. This research holds significant importance for improving early diagnosis and treatment of brain tumors and provides new possibilities for the development of the medical image analysis field.

    Keywords: brain tumors, target detection, YOLOv8, Inner-GIoU, CBAM

    Received: 08 Jun 2024; Accepted: 01 Aug 2024.

    Copyright: © 2024 Chen, Lin and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Da Lin, Inner Mongolia University, Hohhot, China

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