CICADA (UCX): A novel approach for automated breast cancer classification through aggressiveness delineation.

Breast cancer remains one of the leading causes of mortality worldwide, with current classification and segmentation techniques often falling short in accurately distinguishing between benign and malignant cases. The study both emphasize the novel approach, CICADA (UCX), specifically designed for breast segmentation with a focus on delineating aggressiveness. While the title highlights segmentation, the abstract expands on this by detailing the model's effectiveness in enhancing diagnostic precision in classifying aggressive tumor characteristics.

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.