Hybrid Quantum-Classical Liver Tumor Detection

🚀 Overview

This project focuses on detecting liver tumors using a hybrid quantum-classical approach.
It utilizes the 3D-IRCADb1 dataset, which contains annotated liver CT scans, for model training.

The system performs liver and tumor segmentation using deep learning models like U-Net,
combined with quantum-inspired algorithms for enhanced image processing.

The model classifies liver tumors as malignant or benign, leveraging quantum-based image segmentation
techniques to improve accuracy.

🏥 Key Features

• ✅ Liver and Tumor Segmentation using U-Net
• ✅ Quantum-Inspired Image Processing for enhanced accuracy
• ✅ Malignant vs. Benign Tumor Classification
• ✅ 3D-IRCADb1 Dataset for training and evaluation
• ✅ Aiding Radiologists in Early Diagnosis

📌 Work in Progress 🚧

This project is currently under development and will be released soon!
Stay tuned for updates.

📂 Dataset

• 3D-IRCADb1 (Liver CT scans with annotations)

⚡ Technologies Used

• Deep Learning: U-Net
• Quantum Computing: Quantum-Inspired Segmentation
• Dataset: 3D-IRCADb1
• Programming: Python, TensorFlow

📅 Coming Soon!

We are actively working on this project. Follow for updates! 🚀