1 results listed
Glioma is one of the most common and aggressive
types of brain tumors. This study aims to predict the glioma type
and its evolutionary process using the YOLOv8 deep learning
model based on segmented MR images. The segmented MR images
obtained from the BraTS dataset were processed into 2D slices
using a custom algorithm. The model was trained using transfer
learning, and the Adam optimizer was employed for optimization.
The model's performance was evaluated using YOLOv8's
standard metrics, including mAP, IoU, Precision, and Recall. The
results demonstrate that the YOLOv8 model trained on 2D data
derived from segmented images achieved 98.5% accuracy, 98.5%
F1 score, and 88% sensitivity, effectively classifying glioma types
and reliably predicting the evolutionary process.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Rashid Hamidov
Ferhat Atasoy