DETECTION OF BRAIN TUMORS USING ADVANCED IMAGE PROCESSING AND THE ENSEMBLE MODEL AND YOLO FAMILY

Effective diagnosis and therapy of brain tumors depend on their identification and classification. Using the Brain Tumor dataset, this study makes use of sophisticated transfer learning models and deep convolutional neural networks (DCNNs). When models like DCNN, ResNet152, EfficientNetB2, Xception, and NasNetMobile were tested, an ensemble of Xception and NasNetMobile produced the best accuracy (98.1%). Grade 0 (no malignancy) to Grade III (big tumor) were the four grades into which tumors were divided. With a mean average precision (mAP) of 78.9%, YOLOv9 fared better than other models for anomaly detection. A Flask-based interactive interface was created for safe and easy access in order to improve usage.