Enahncing Brain Tumor Detection Accuracy Through An Assemble Of Yolov5 And Segemnt Any Thing Model(SAM)

Specificity in tumor detection means improved diagnosis and planning of treatment, hence better medical imaging and patient care. This Review proposes the cluster model for brain tumor detection, followed by its classification accuracy of Segment Anything Model for YOLOv5 to detect it faster. This approach harnesses the complementary strengths of both models for a detection system that is, on the whole, more efficient, as tested by the Ensemble model on benchmark datasets, including a comprehensive brain tumor dataset, and exhibits better performance in terms of accuracy, precision, recall, and F1 scores compared to individual models. The results underline the fact that ensemble models in complex models, by tumor morphology and imaging conditions, are more effective in medical imaging to dramatically improve diagnostic methods and increase the efficiency of smart health care systems. This new approach combines the state-of-the-art machine learning algorithms for better sensitivity and specificity in brain tumor identification. The proposed cluster model arranges the data in an orderly fashion, thus making possible much more accurate segmentation and analysis. On the other hand, the Segment Anything Model brings in robustness to the process of detection and identifies different types of tumors across different modalities of imaging, such as MRI and CT scans. This makes it very versatile and accommodates the variability inherently found in brain tumor manifestations. Finally, YOLOv5 brings acceleration to the process of detection and helps enable real-time analysis, which is quintessential for urgent medical decision-making. The Ensemble model used multiple algorithms to cross-validate results for the reduction of false positives in the diagnosis. This way, the layered approach will offer sufficient evaluation of the imaging data, which is critical considering the complexity of brain tumors. The experimental testing with benchmark datasets proves that the framework of combined models is better than traditional approaches with single models. The ensemble model, when applied, not only increases the metric performance—accuracy, precision, recall, and F1 score—but also increases its adaptability to new, unseen imaging conditions, a frequent challenge in medical diagnostics. By advancing the capability of diagnostic imaging technologies, this model looks toward a transition to more intelligent and effective healthcare systems. Such advanced analytical tools integrated with medical imaging are sure to change the future course of the field in terms of early and accurate diagnosis, personalized plans for therapy, and overall improvement in patient outcomes.