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.
