Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sai Sakunthala Guddanti, Apurva Padhye, Anil Prabhakar, Sridhar Tayur
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引用次数: 1

Abstract

Early diagnosis of pneumonia is crucial to increase the chances of survival and 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 study, we offer a comparison between different methods: (1) a 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 found that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, competitive with LibSVM, and superior to Gurobi and quantum annealing.
通过二元分类检测肺炎:支持向量机 (SVM) 的经典、量子和混合方法
肺炎的早期诊断对于提高患者存活率和缩短康复时间至关重要。胸部 X 光图像是实践中使用最广泛的方法,但其分类却具有挑战性。我们的目标是开发一种机器学习工具,能够准确地将图像分类为属于正常人还是感染者。支持向量机(SVM)之所以具有吸引力,是因为二元分类可以表示为一个优化问题,尤其是二次无约束二元优化(QUBO)模型,而QUBO模型又可以自然地映射到伊辛模型,从而使经典退火、量子退火和混合退火成为一种有吸引力的探索方法。在本研究中,我们对不同的方法进行了比较:(1) SVM 最先进的经典实现(LibSVM);(2) 使用经典求解器(Gurobi)求解 SVM,包括分解和不分解;(3) 使用模拟退火求解 SVM;(4) 使用量子退火(D-Wave)求解 SVM;(5) 使用格雷弗增强多种子算法(GAMA)求解 SVM。我们使用模拟退火和量子退火两种方法,尝试了几种不同的 Graver 元素数量和种子数量的 GAMA 算法。我们发现,模拟退火和 GAMA(使用模拟退火)具有可比性,能快速提供准确结果,与 LibSVM 相比具有竞争力,并且优于 Gurobi 和量子退火。
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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