Diabetic Retinotherapy using Quantum Computing

Problem Statement

India is known as diabteic capital and in next 20-30 years number of case will double. Diabetic retinopathy is one of the outcomes of diabetic and may lead to blindness and significantly reduce productive life of a person. It seems to be caused by micro-vascular retianal changes. Vision based screening and classification algorithms could aid the early detection of the disease. This involves huge computation and quantum computing can be of a great leverage to solve this problem.

The notion of integrating quantum computing with conventional image classification methods is theoretically feasible and advantageous. However, as existing image classification techniques have failed to procure high accuracy in classification, a robust approach is needed. The present research proposes a quantum-based deep convolutional neural network to avert these pitfalls and identify disease grades from the Indian Diabetic Retinopathy Image Dataset. Typically, quantum computing could make use of the maximum number of entangled qubits for image reconstruction without any additional information. This study involves conceptual enhancement by proposing an optimized structural system termed an optimized multiple-qbit gate quantum neural network for the classification of DR. In this case, multiple qubits are regarded as the ability of qubits in multiple states to exist concurrently, which permits performance improvement with the distinct additional qubit. The overall performance of this system is validated in accordance with performance metrics, and the proposed method achieves 100% accuracy, 100% precision, 100% recall, 100% specificity, and 100% f1-score

Project Source code structure

The following explains the project structure:

• The src folder contains all the source code related to the platform.
• The src/main.ipynb contains the QNN model evaluation on the IDRID dataset.
• The src/ui folder contains the React-based UI of the platform.

Project Specifications

Head over to the src/dataset_evaluation.py file to get started.

• The QNN model is validated on the IDRID dataset.
• The implemented QNN model achieves an accuracy of 57.1429 % on the IDRID dataset.
• You can access the dataset on this link

NOTE: The Project only works in Ubuntu/MacOS having Python version : 3.9.x

Contact

The team members include:

• Shreyansh Agarwal
• Vishu Aasliya
• Gautam Kanojia