V3DQutrit a volumetric medical image segmentation based on 3D qutrit optimized modified tensor ring model.
Authors
Affiliations (10)
Affiliations (10)
- CSE Department, NIT Kurukhetra, Kurukhetra, Hariyana, India.
- CSE Department, Galgotias University, Greater Noida, Uttar Pradesh, India.
- CSE Department, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India.
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia. [email protected].
- Department of Solar, Al-Nahrain Research Center for Renewable Energy, Al-Nahrain University, Jadriya, Baghdad, Iraq.
- Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan.
- Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia.
- Jadara Research Center, Jadara University, Irbid, 21110, Jordan.
- Faculty of Computers and Artificial Inellgence, Benha University, Benha, Egypt.
- CSE Department, IGDTU, Delhi, India.
Abstract
This paper introduces 3D-QTRNet, a novel quantum-inspired neural network for volumetric medical image segmentation. Unlike conventional CNNs, which suffer from slow convergence and high complexity, and QINNs, which are limited to grayscale segmentation, our approach leverages qutrit encoding and tensor ring decomposition. These techniques improve segmentation accuracy, optimize memory usage, and accelerate model convergence. The proposed model demonstrates superior performance on the BRATS19 and Spleen datasets, outperforming state-of-the-art CNN and quantum models in terms of Dice similarity and segmentation precision. This work bridges the gap between quantum computing and medical imaging, offering a scalable solution for real-world applications.