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

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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