Brain Tumor Detection by Aggregating Deep Learning and GAN Models for Faster MRI image Synthesis - Saryu Verma; Jatin Arora (International Journal of Brain Sciences, 2025)
Brain tumors comprise a global health challenge that, in order to be treated and organized, need early and accurate diagnosis. Usually conducted through medical imaging, brain tumor detection techniques have problems of accuracy, efficiency, and confidentiality. Issues of limited datasets, strict privacy laws that provide restrictions on data sharing, and the necessity for specialized expertise on medical image analysis relegates modern methodologies to vulgar charades. For patient prognosis, treatment planning, and diagnosis, accurate brain tumor identification is key. To boost the medical imaging brain cancer detection, we suggest a novel hybrid technique within this work based on ResNet 50 and synthetic GANs. In developing several artificial brain scans that look like actual images of tumors, we explore Augmentation techniques that are critical to expanding and diversifying our dataset. Such methodology for augmenting datasets is crucial when it comes to train models when there is a scarcity of quality medical images.
Keywords: Brain tumor images; Deep learning. ResNet architecture; GAN; MRI; ResNet50