A novel SLCA-UNet architecture for automatic MRI brain tumor segmentation

Abstract

When it comes to brain tumors, there’s no other disease that has as heavy an impact on life expectancy, and not only is it among the main causes of death globally. The only way out of this is through prompt identification and prediction of brain tumors to reduce related deaths. MRI remains the conventional imaging method used; however, manually segmenting its images can take time, hence taking long periods before a diagnosis is made. A potential answer to this challenge has been found in deep learning models based on the UNet architecture, which seems promising for automating biomedical image analysis. However traditional UNet models are complicated as they struggle with accuracy and processing related information contextually. Therefore, we present Scleral Residue Class Attention UNet (SLCA-UNet), an improved version of UNet incorporating, among others, residual dense blocks, layered attention, and even channel attention modules into it, thus making it capable of capturing wide and thin features more efficiently than before. The results from experiments conducted on the Brain Tumor Segmentation Dataset 2020 indicated that the SLCA-UNet performed well in terms of indistinct metrics, showcasing its usefulness when it comes to automatic brain tumor segmentation. This development is one step further compared to other ways used so far since there’s gained better precision as well as faster detection options available for tumors than ever before.