Liver tumor detection using YOLOv8: size-adaptive nanoparticle design based on tumor dimensions

Today, liver tumors have become a crucial and popular problem in our current era. Advances in artificial intelligence, especially computer vision, have enhanced diagnostic capabilities and provided deeper insights into tumor features. In this study, we leverage YOLOv8s for liver tumor detection, using MRI images to identify tumors with bounding box annotations and extract critical features such as shape, size, and diameter. These features are then used to determine the optimal nanoparticle size based on predefined rules, ensuring a tailored approach to treatment. Our framework demon- strates how AI can bridge the gap between tumor detection and nanoparticle design. The study revealed that tumor diameter significantly influences the optimal nanoparticle design. For tumor diameters of 257.99 µm, 297.47 µm, and 422.3 µm, the corresponding nanoparticle sizes were 729.0 nm, 535.0 nm, and 447.0 nm, respectively. Experimental results confirm the detection system's accuracy and effectiveness in recommending nanoparticle sizes, aiding liver tumor diagnosis and treatment.