Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery.

Breast cancer: A comparative review for breast cancer detection using machine learning techniques.

Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer.