Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans

Abstract

Brain tumors, characterized by abnormal cell growth, pose a significant challenge in clinical imaging due to their complex and diverse structures. Early and accurate identification, classification, localization, and segmentation of these tumors are critical to reducing mortality. However, the extensive data generated by MRI scans makes manual segmentation time-consuming and impractical for clinical use. To address these challenges, we propose, a hybrid deep learning model that precisely segmented tumor regions with U-Net to enable YOLOv8s to efficiently detect, classify and localize tumors. The model was trained and validated using The Cancer Imaging Archive (TCIA) dataset, which includes MRI images of brain tumors, and the Cancer Genome Atlas (CGA) low-grade glioma dataset, which includes data from 110 patients with FLAIR aberrant segmentation masks. The proposed hybrid model was evaluated using several performance metrics, including F1 score, specificity, recall, precision, accuracy, and ROC-AUC score. Hybrid proposed performed highly, achieving a precision of 97.8 %, accuracy of 98.6 %, recall of 95.2 %, F-1 score of 96.3 %, specificity of 89.1 %, and ROC-AUC score of 98.5 %. The integration of YOLOv8s and U-Net in Enhanced TumorNet offers a powerful solution for the automated analysis of brain tumors in MRI scans, significantly improving detection and segmentation accuracy. This hybrid approach holds great potential for clinical applications, enhancing the efficiency and effectiveness of brain tumor diagnosis.

Keywords

• Brain tumor detection
• MRI segmentation
• YOLOv8s
• U-net model
• Hybrid deep learning model
• Medical imaging