Detection of Brain Tumor Using H-DenseAttentionUNet with MRI Images

Abstract

Accurate and efficient identification of brain tumors is essential for the diagnosis of the illness and the creation of patient-centered medications. In this study, we propose a novel approach for brain tumor detection utilizing the H-DenseAttentionUNet architecture, a hybrid model incorporating elements from U-Net, densely connected networks, and attention mechanisms. The designed model is specifically tailored for the examination of Magnetic Resonance Imaging (MRI) data, leveraging the superior soft tissue contrast and intricate anatomical details provided by this imaging modality. The H-DenseAttentionUNet architecture is characterized by its ability to capture intricate details through densely connected blocks, while attention mechanisms enhance the network's focus on salient features within the MRI images. The model aims to provide precise segmentation and localization of brain tumors, facilitating a comprehensive understanding of tumor boundaries and characteristics. The H-DenseAttentionUNet shows a high degree of accuracy in the exact identification of brain cancers from MRI scans, according to initial findings. The proposed approach holds promise for enhancing the efficiency and precision of brain tumor detection, providing clinicians with valuable insights for timely and informed decision-making in patient care.