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A Lie group kernel learning method for medical image classification
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DOI：10.1016/j.patcog.2023.109735.png)
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Abstract
En 中文
Medical image classification is a basic step in medical image analysis and has been an essential task in computer-aided diagnosis. Existing classification methods are proved to be effective in conventional im-age classification tasks, but they often achieve a suboptimal performance when applied to medical images characterizing by complex nonlinear variation. Aiming at this challenge, this paper proposes a Lie group kernel learning method for medical image classification by combining Lie group theory, kernel functions, SVM and KNN classifiers. The method represents each image with a Lie group feature descriptor con-structed from low-level features and builds a SVM classifier from the training images. Geodesic distances between categorical pivots and each testing image are calculated with Lie group kernel functions to se-lect either the SVM or a KNN classifier to do the classification. The proposed method is applied to three medical image datasets and the results demonstrate the efficacy of the method. (c) 2023 Elsevier Ltd. All rights reserved.
Keywords:
Medical image classification
Feature representation
Lie group manifold
Lie group machine learning
Kernel learning