Reconstruction, Identification, Classification and Segmentation of Brain Tumor Using GAN and Faster R-CNN

This study introduces an innovative framework for the reconstruction, identification, segmentation, and classification of brain tumors utilizing Generative Adversarial Networks (GANs) and Faster Region-based Convolutional Neural Networks (Faster R-CNN). The approach begins with GANs to enhance the quality of MRI scans, effectively mitigating issues such as noise and inconsistencies. Subsequently, Faster R-CNN is employed for precise tumor detection and segmentation, enabling accurate localization of tumor regions. Our method not only identifies various tumor types but also generates detailed segmentation maps, significantly improving the accuracy of clinical assessments. Experimental results demonstrate that this integrated model outperforms traditional methods in both accuracy and robustness, underscoring its potential to support radiologists in clinical decision-making and enhance patient care. This research marks a significant advancement in medical imaging technologies, facilitating more effective diagnosis and management of brain tumors. Keywords - Brain tumor, Generative Adversarial Networks, Faster R-CNN, image segmentation, medical imaging, tumor classification, MRI enhancement, deep learning.