Publication

Abstract : Brain tumors are a significant threat to health in all parts of the world and are always causing serious morbidity and mortality. The key aspects that have been identified and that have to do with an early and accurate diagnosis include a very important view that notable progress in addressing and treating cancer mainly stems from improved diagnosis. The goal is to use cutting-edge computer vision technologies to increase the precision of brain tumor identification and classification. Using state-of-the-art models such as EfficientNet, GoogleNet, Xception, and VGG16 for classification, alongside models like ResNet50, ResUNet, and U-Net for segmentation, this method utilizes deep learning to diagnose MRI images effectively. Not only does it provide correct classifications of tumor types, it also outlines the margins of the tumor, making it easier to characterize tumors. Incorporation of these models is the goal of the research to solve general dilemmas associated with medical application of image processing and analysis such as variability of tumor manifestation and large image data processing. In addition to the physical improvement that is aimed at increasing the diagnostic accuracy of the methods, this innovative framework is also expected to accelerate interventions for the benefit of patients. Such a conclusion of this project may bring groundbreaking change in contemporary clinical practices, which in turn will help in making better decisions and achieving higher positive results in the quality of patients’ lives diagnosed with brain tumors. The objective is to gain substantial advancement in the area of medical images, and therefore it can provide practical contributions in the classification and detection of brain tumors using advanced technological approaches.