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The research on medical image classification algorithm based on PLSA-BOW model

Issue title: Papers from the 4th International Conference on Biomedical Engineering and Biotechnology (ICBEB2015), 18-21 August 2015, Shanghai, China ``New Paradigms for Biomedical Engineering and Biotechnology''

Article type: Research Article

Authors: Cao, C.H.a; b; c; * | Cao, H.L.a; b; c

Affiliations: [a] College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China | [b] Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, Liaoning, China | [c] State Key Lab. for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China

Correspondence: [*] Corresponding author: C.H. Cao, College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China. Tel.: +86 15502406519; E-mail:[email protected]

Keywords: Medical image classification, bag of words model, PLSA

DOI: 10.3233/THC-161194

Journal: Technology and Health Care, vol. 24, no. s2, pp. S665-S674, 2016

Published: 13 June 2016
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Abstract

BACKGROUND:

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment.

OBJECTIVE:

To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model.

METHODS:

In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model.

RESULTS:

The method enables the word bag model-based classification method to be further improved in accuracy.

CONCLUSIONS:

The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

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