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3 October 2022 Quantum machine learning prediction model for retinal conditions: performance analysis
Manoj R. Naick, Mohammed Abdul Rasheed, Varadharajan Jayakumar, Janarthanam Jothi Balaji, Vasudevan Lakshminarayanan
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Proceedings Volume 12227, Applications of Machine Learning 2022; 122270V (2022) https://doi.org/10.1117/12.2633672
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States
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Abstract
Quantum machine learning by superposition and entanglement, for disease categorization utilizing OCT images will be discussed in this paper. To the best of our knowledge, this is the first application of the quantum computing element in a neural network model for classifying ophthalmological disease. The model was built and tested with PennyLane (PennyLane.ai), an open-source software tool based on the concept of quantum differentiable programming. The model training circuit functioning was tested on an IBM 5 qubits system “ibmq_belem” and a 32 qubits simulator “ibmq. qasm_simulator”. A hybrid quantum and classical model with a 2 qubit QNode converted layer with operations such as Angle Embedding, BasicEntanglerLayers and measurements were the internal operations of the qlayer. Drusen, Choroidal neovascularization (CNV), and Diabetic macular edema (DME) OCT images formed the abnormal/disease class. The model was trained using 414 normal and 504 abnormal labelled OCT scans and the validation used 97 and 205 OCT scans. The resulting model had an accuracy of 0.95 in this preliminary 2-class classifier. This study aims to develop a 4-class classifier with 4 qubits and explore the potential of quantum computing for disease categorization. A preliminary performance analysis of quantum Machine Learning, the steps involved, and operational details will be discussed.
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Manoj R. Naick, Mohammed Abdul Rasheed, Varadharajan Jayakumar, Janarthanam Jothi Balaji, and Vasudevan Lakshminarayanan "Quantum machine learning prediction model for retinal conditions: performance analysis", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270V (3 October 2022); https://doi.org/10.1117/12.2633672
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KEYWORDS
Machine learning
Optical coherence tomography
Quantum computing
Image classification
Image processing
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