Segmentation of Thoracic Organs through Distributed Extraction of Visual Feature Patterns Utilizing Resio-Inception U-Net and Deep Cluster Recognition Techniques.

Background: Segmentation of medical images plays a key role in the correct identification and management of different diseases. In this study, we present a new segmentation method that meets the difficulties posed by sophisticated organ shapes in computed tomography (CT) images, particularly targeting lung, breast, and gastric cancers.

Methods: Our suggested methods, Resio-Inception U-Net and Deep Cluster Recognition (RIUDCR), use a Residual Inception Architecture, which combines the power of residual connections and inception blocks to achieve cutting-edge segmentation performance while reducing the risk of overfitting.