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Cited by 35Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesAI in cancer detection

Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model

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Chen, Aruna, Lin, Da, Gao, Qiqi·2024·Frontiers in Neurology
DOI 10.3389/fneur.2024.1445882PMC PMC11374633
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Abstract

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.

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  • The use of data augmentation strengthens the model's ability to identify tumors in complex orientations, leading to better treatment planning.

    “The use of data augmentation strengthens the model's ability to identify tumors in complex orientations, leading to better treatment planning [62,63].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [63]

    Section 3.2 (page unavailable) says YOLOv8 uses a data-augmentation strategy and links the model’s improvements to more accurate brain-tumor detection and support for treatment. It does not say augmentation specifically improves detection in complex orientations or establish that augmentation itself leads to better treatment planning. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • YOLOv3, paired with data augmentation techniques like 180° and 90° rotations, has proven effective in brain tumor detection and segmentation.

    “YOLOv3, paired with data augmentation techniques like 180 ◦ and 90 ◦ rotations, has proven effective in brain tumor detection and segmentation.”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [63]

    Section 2.2 (page not available) says YOLOv3 improved the speed and accuracy of brain tumor detection in MRI images. It does not link YOLOv3 to 180° or 90° rotations or say it was effective for segmentation; Section 3.2 discusses data augmentation for YOLOv8 instead. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Chen, A., Lin, D., & Gao, Q.
Affiliations
  • Ministry of Education of the People's Republic of China (CN)
  • Inner Mongolia University (CN)
  • Inner Mongolia Normal University (CN)
Corresponding author
Lin, Da
ORCID
Venue
Frontiers in Neurology
Publisher
Frontiers Media SA
Published
2024-08-22
Volume
15
Pages
1445882-1445882
ISSN
1664-2295 (ISSN-L)
DOI
10.3389/fneur.2024.1445882
PMC
PMC11374633
OpenAlex
W4401788330
Cited by
35
References
1
Citations by year
2026: 18 · 2025: 14 · 2024: 3
FWCI
5.46
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Neurology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
brain tumor detection, MRI images, YOLOv8, object detection, CBAM attention mechanism, dynamic convolution kernels, tumor localization, medical image analysis

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