Paper Contents
Abstract
Brain tumors are among the most serious health issues, impacting people of all ages. Radiologists face difficulties in accurately identifying and segmenting brain tumors due to their intricate shapes and significant variability. Deep learning has become a crucial technology in the realm of automated disease detection, offering innovative approaches to support radiologists and enhance diagnostic precision. In this study, we investigate the use of the UNet architecture for the automated segmentation and classification of brain tumors in MRI images. The UNet model, recognized for its encoder-decoder framework with skip connections, is particularly effective at capturing detailed features, making it well-suited for tasks involving biomedical image segmentation. Our research addresses essential phases in implementing UNet, which encompass data preprocessing, segmentation, and model training. We also explore the impact of data augmentation techniques on enhancing model performance and resilience. In addition, we address the incorporation of automated brain tumor detection techniques into intelligent healthcare systems, highlighting their ability to improve personalized patient care and clinical processes. Lastly, the paper presents future research avenues aimed at further enhancing the accuracy, generalizability, and clinical relevance of UNet-based methods for brain tumor diagnosis.
Copyright
Copyright © 2024 Yadla Praveen Kumar. This is an open access article distributed under the Creative Commons Attribution License.
