Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM) - Guddanti, Sai Sakunthala; Padhye, Apurva; Prabhakar, Anil; Tayur, Sridhar (Frontiers in Computer Science, 2024)
Early diagnosis of pneumonia is crucial to increase the chances of survival and to reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine-learning tool that can accurately classify images as belonging to normal or infected individuals. A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing – classical, quantum and hybrid – an attractive approach to explore. In this paper, we offer a comparison between different methods: (1) bluea classical state-of-the-art implementation of SVM (LibSVM); (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds, using both simulating annealing and quantum annealing. We find that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, bluecompetitive with LibSVM, and superior to Gurobi and quantum annealing.