Enhanced Brain Tumor Detection Using Resnet-50

Brain tumors are major threat to human health and life. It is better for early detection and treatment the tumors to enhance survival rates. Traditional methods of detecting and categorizing brain tumor, such as manual segmentation and feature extraction, they are both time-consuming and prone to inaccuracies. In this context, Resnet50 enhanced detecting of brain tumor has emerged as a promising alternative for brain tumor classification. Deep learning models have the capacity to autonomously acquire pertinent features from medical images, like MRI scans, and assign them to various categories. This project introduces a unique methodology to enhance brain tumor detection. utilizing ResNet-50 as a feature extractor to capture intricate spatial hierarchies within brain magnetic resonance imaging (MRI) scans. These extracted features are then fed into a customized CNN tailored for precise tumor classification, and then assessed its performance on a separate test dataset. The findings demonstrate that the fine-tuned ResNet50 model achieved exceptional accuracy in classifying brain tumors, with a remarkable accuracy rate of $90.04 %$ on the test dataset. These results strongly suggest that deep learning models, particularly the combination of CNNs and ResNets, hold great potential for the development of precise and efficient brain tumor classification systems. this project introduces a robust and innovative solution for brain tumor detection through a hybrid ResNet-50 and CNN model. The potential clinical impact includes faster and more accurate diagnoses, promising improved outcomes This advancement could significantly enhance the early detection and treatment of brain tumors, ultimately leading to improved outcomes for patients. The abstract can be tailored to the specific content of the report, including more detailed information about the research methods and outcomes, and potential applications for clinical use.