| Intelligent Multi-Class Brain Tumor Detection and Localization Using Majority Voting and Object Detection |
| کد مقاله : 1352-NAEC |
| نویسندگان |
|
Mojtaba Gandomkar *1، Sahar Khoramipour2 1دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی جندیشاپور دزفول، دزفول، ایران 2نننننن |
| چکیده مقاله |
| Deep learning approaches have emerged as powerful tools for automating brain analysis for identification and precise localization of brain tumors, which is important in treatment planning. In this study, a robust ensemble-based framework is proposed for brain tumor detection and localization in magnetic resonance imaging (MRI) scans. A majority voting strategy is employed to fuse the predictions of three complementary deep learning models: a multi-channel convolutional neural network (CNN), a hybrid CNN combined with a support vector machine (SVM) classifier, and a YOLO-based object detection network. Experimental results demonstrate that the ensemble strategy significantly improves tumor detection and classification performance compared to individual models. The proposed method achieves an accuracy of 99% and a precision of 100%, indicating its high robustness and consistency. Furthermore, the integration of the YOLO-based detector enables accurate spatial localization of tumor boundaries. The object detection component specifically achieves an F1-score of 92.25%, and a mean average precision (mAP) of 94.9% at IoU threshold 0.5 (mAP50) and 69.7% across IoU thresholds 0.5–0.95 (mAP50–95). These results confirm the model’s effectiveness in delineating tumor regions with high spatial fidelity. |
| کلیدواژه ها |
| : Brain Tumor, Convolutional Neural Network (CNN), Support Vector Machine (SVM) Classifier, Majority Voting, YOLO Object Detection Model. |
| وضعیت: پذیرفته شده |
