Leveraging Multiscale Level Set Algorithm With Adaptable and Attention‐Based Graph Network Model for Fine‐Grained Segmentation Over Medical Images

Accurate segmentation of tumor regions is vital for detecting lesions in medical scans. It helps doctors diagnose conditions early, plan treatments effectively, and monitor disease progression. This process is especially important in radiology, oncology, and dermatology. Deep learning has made great strides in automating medical image investigation. However, many existing systems at rest resist the noisy data and complex anatomical structures. Low contrast and irregular shapes often lead to inaccurate segmentation outcomes. The aforementioned disadvantages can affect clinical decisions and delay proper treatment. To overcome these challenges, a more adaptive and reliable approach is needed. Such a method should work well across different imaging conditions and patient populations. Improving segmentation accuracy can directly enhance patient care and outcomes. Therefore, in this work, an effective image segmentation technique is developed using an advanced deep learning model for medical imaging, which enables the framework to achieve fine on small lesion segmentation tasks. At first, the necessary images are collected from reputable sources. Then, the gathered images are passed to the Multiscale Level Set Algorithm (MLSA) for the accurate segmentation of medical images. Here, the proposed model explores the use of Partial Differential Equations (PDEs) in boundary detection and contour evolution for organ and tissue segmentation. Particularly, the PDEs evolve a level set function, which implicitly defines a curve or surface within the medical images. Moreover, the Adaptive and Spatial Attention‐based Graph UNet++ (ASA‐GUNet++) is proposed for fine‐grained segmentation. Here, the parameters of the ASA‐GUNet++ model are tuned using the Improved Guidance Strategy of Superb Fairy‐Wren Algorithm (IGSSA) for improving segmentation performance regarding accuracy. The proposed model is more useful for handling challenges like small lesion segmentation, irregular boundaries, and class imbalance because of the innovative deep learning model integrated with the optimization algorithm. Finally, the proposed model has undergone performance validation to evaluate its performance across various medical imaging modalities.