Reflect of 3d brain tumor segmentation and classification

Abstract

This comprehensive study delves into the potential of advanced deep learning techniques for the critical task of segmenting and classifying brain tumors from magnetic resonance imaging (MRI) scans. The research employs a series of sophisticated neural network architectures, with a particular focus on the VGG16, ResNet152V2, and ResNet50 models, alongside an innovative VGG19-UNet hybrid model. The study's objective was to assess the feasibility and effectiveness of these models in automating the complex process of identifying and delineating brain tumors, which is a pivotal step in the diagnostic and treatment planning process for neuro-oncological conditions. The methodology involved a meticulous process of data preparation, involving the acquisition of a curated dataset, preprocessing, and augmentation to facilitate optimal model training. The models underwent a rigorous training regimen, leveraging the power of transfer learning to refine their segmentation capabilities. Each model's performance was critically evaluated based on a suite of metrics, including test accuracy, loss percentage, precision, recall, and the F1 score, with the intent of identifying the most accurate and reliable approach for clinical application. In this endeavor, the study uncovered that the VGG16 and ResNet152V2 models showed superior performance, with the ResNet152V2 model marginally outperforming all others in precision and recall. The VGG19-UNet hybrid model, with its integration of VGG19's depth in feature extraction and UNet's precision in spatial localization, emerged as a particularly potent tool for the finegrained task of tumor segmentation.