Generative and Discriminative Learning for Lung X-Ray Analysis Based on Probabilistic Component Analysis

Abstract

Khalaf Alshamrani,1,2 Hassan A Alshamrani,1 F F Alqahtani,1 Ali H Alshehri,1 Saleh Hudayban Althaiban3 1Radiological Science Department, Najran University, Najran, Saudi Arabia; 2Oncology and Metabolism Department, Medical School, University of Sheffield, Sheffield, United Kingdom; 3Department of Radiology, New Najran General Hospital, Najran, Saudi ArabiaCorrespondence: Khalaf Alshamrani, Email [email protected]; [email protected]: The paper presents a hybrid generative/discriminative classification method aimed at identifying abnormalities, such as cancer, in lung X-ray images.Methods: The proposed method involves a generative model that performs generative embedding in Probabilistic Component Analysis (PrCA). The primary goal of PrCA is to model co-existing information within a probabilistic framework, with the intent to locate the feature vector space for X-ray data based on a defined kernel structure. A kernel-based classifier, grounded in information-theoretic principles, was employed in this study.Results: The performance of the proposed method is evaluated against nearest neighbour (NN) classifiers and support vector machine (SVM) classifiers, which use a diagonal covariance matrix and incorporate normal linear and non-linear kernels, respectively.Discussion: The method is found to achieve superior accuracy, offering a viable solution to the class of problems presented. Accuracy rates achieved by the kernels in the NN and SVM models were 95.02% and 92.45%, respectively, suggesting the method’s competitiveness with state-of-the-art approaches.Keywords: generative learning, discriminative learning, probabilistic component analysis, nearest neighbour classifier, support vector machines classifier

Keywords

• generative learning
• discriminative learning
• probabilistic component analysis
• nearest neighbour classifier
• support vector machines classifier.