OBJECT DETECTION AND ENHANCED IN BRAIN TUMORS : ODTWCHE

Brain tumors present critical challenges in diagnostics due to their complexity and reliance on radiologists' manual interpretation of medical imaging, which is prone to errors. This study leverages deep learning (DL) techniques to enhance the classification and detection of brain tumors. A robust framework employing convolutional neural networks (CNNs), transfer learning with VGG16 and ResNet50, and advanced data augmentation was developed. Utilizing the Brain Tumor Segmentation (BRATS) datasets, the models demonstrated up to 95% accuracy, surpassing traditional methods. Error analysis revealed key challenges, such as blurry tumor boundaries and suboptimal image quality, guiding further preprocessing improvements. Despite promising results, limitations include the reliance on public datasets and the need for clinical validation. Future work will expand datasets, integrate multimodal imaging, and explore advanced architectures. The study underscores DL’s potential to support radiologists, enhancing diagnostic accuracy and patient outcomes in brain tumor detection.