I. Introduction
In recent years, clinical imaging improvements have enabled a more excellent knowledge of sickness development and treatment possibilities. However, extracting meaningful statistics from clinical pics remains challenging because of their complicated nature[1]. Some pc-aided procedures have been proposed to help physicians pick out essential features from medical images. One of the maximum prominent of those tactics has been medical image segmentation. Scientific photograph segmentation subdivides a photo into significant elements that may then generate exact measurements or descriptions of the affected vicinity. This process can offer meaningful insights into the affected person's pathological adjustments that may aid in the analysis and remedy-making plans[2]. But automated clinical picture segmentation is confined via its incapability to capture context-aware features, which could result in misguided or incomplete segmentations. Researchers have proposed incorporating interest-based recurrent neural networks (RNNs) into segmentation models to improve scientific picture segmentation. These fashions are designed to introduce contextual relevance into segmentation, permitting them to higher become aware of and distinguish critical systems[3]. It is finished by incorporating some interest mechanisms that leverage spatial and temporal capabilities. By introducing attention-based RNNs into clinical photo segmentation, physicians have to get the right of entry to better satisfactory and more correct segmentations, which can be used to tell diagnostic selections. Incorporating interest-based recurrent neural networks into scientific photograph segmentation has several potential benefits. First, the ensuing segmentations offer finer decisions, making it easier to pick out small capabilities from the target photo [4]–[5].