Integrating YOLO Localization and VGG Classification for Enhanced Brain Tumor Diagnosis

Among the severe diseases, brain tumors have to be precisely and quickly detected in order to be effectively treated .An integrated detection method that combines the VGG model (Visual Geometry Group) networks for precise tumor type classification with a YOLO (You Only Look Once) algorithm for real-time tumor location. The model efficiently recognizes and categorizes cancer in MRI scans by using the speed of YOLO and the high accuracy of VGG. In contrast with conventional methods, experiments show improved detection accuracy, fewer false positives, and quicker processing times. The suggested strategy might be a vital tool for medical diagnostics, helping radiologists makes quick and precise judgments. Recently, the YOLO for detection and the VGG for classification have generated interest due to their ability to increase medical image processing speed and accuracy. In these hybrid models. VGG is applied to the identified locations .YOLO first locates the tumor by forecasting bounding boxes around possible tumor regions. VGG provides accurate tumor type classification, while YOLO provides rapid tumor position. Improved Accuracy: The hybrid method often offers higher precision in both recognition and categorization tasks through combining the positive aspects of both models. The proportion of both malignant and benign tumor samples is frequently imbalanced, which could hinder model performance. It is overcome by using this model. YoloVGG(Proposed) model Detection Accuracy is 92.5. Classification Accuracy 94.8.Performance is high when compared to the other Model