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Identifying the extent of brain tumors is a significant challenge in braincancer treatment. The main difficulty is in the approximate detection of tumorsize. Magnetic resonance imaging (MRI) has become a critical diagnostic tool.However, manually detecting the boundaries of brain tumors from MRI scans is alabor-intensive task that requires extensive expertise. Deep learning andcomputer-aided detection techniques have led to notable advances in machinelearning for this purpose. In this paper, we propose a modified You Only LookOnce (YOLOv8) model to accurately detect the tumors within the MRI images. Theproposed model replaced the Non-Maximum Suppression (NMS) algorithm with aReal-Time Detection Transformer (RT- DETR) in the detection head. NMS filtersout redundant or overlapping bounding boxes in the detected tumors, but theyare hand-designed and pre-set. RT-DETR removes hand-designed components. Thesecond improvement was made by replacing the normal convolution block withghost convolution. Ghost Convolution reduces computational and memory costswhile maintaining high accuracy and enabling faster inference, making it idealfor resource-constrained environments and real-time applications. The thirdimprovement was made by introducing a vision transformer block in the backboneof YOLOv8 to extract context-aware features. We used a publicly availabledataset of brain tumors in the proposed model. The proposed model performedbetter than the original YOLOv8 model and also performed better than otherobject detectors (Faster R- CNN, Mask R-CNN, YOLO, YOLOv3, YOLOv4, YOLOv5, SSD,RetinaNet, EfficientDet, and DETR). The proposed model achieved 0.91 mAP (meanAverage Precision)@0.5.

Brain Tumor Identification using Improved YOLOv8