ArticleFrontiers in neurology2024
Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- BrainYOLO-MCA: An Improved YOLOv11 with Multi-Scale Channel Attention for Brain Tumor Detection.Current medical imaging · 2026Article
- Deep Learning Based Evaluation of Skeletal Maturation: A Comparative Analysis of Five Hand-Wrist Methods.Orthodontics & craniofacial research · 2025Article
- FALS-YOLO: An Efficient and Lightweight Method for Automatic Brain Tumor Detection and Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method.Sensors (Basel, Switzerland) · 2025Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Application and improvement of YOLO11 for brain tumor detection in medical images.Frontiers in oncology · 2025Article
- Application research on YOLOv5 model based on Lightweight Atrous Attention Module in brain tumor MRI image segmentation.Frontiers in medicine · 2025Article
- AI-Assisted surgical vision: evaluating YOLOv8 and YOLOv12 for real-time detection in colon cancer surgery.Frontiers in surgery · 2025Article
- Artificial Intelligence-based Automated Anatomic Landmark Detection and Image Deformation for Nonsmall Lung Cancer Cases.Journal of medical physicsArticle
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3 authors.
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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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