Bio medical image retrieval using support vector machine

2021 ◽  
Author(s):  
S. L. Lekshmi Surya
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Medical Image ◽  
Support Vector ◽  
Medical Image Retrieval

Medical Image Retrieval Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

scholarly journals Heterogeneous Medical Image Retrieval using Multi-Trend Structure Descriptor and Fuzzy SVM Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 3958-3963 ◽  
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Local Level ◽  
Level Structure ◽  
Research Work ◽  
Support Vector ◽  
Svm Classifier ◽  
Fuzzy Support Vector Machine ◽  
Medical Image Retrieval ◽  
Structure Descriptor

This research work contributes a system for heterogeneeous medical image retrieval usiing Multi-trend structure descriptor (MTSD) and fuzzy support vector machine (FSVM) classifier. The MTSD encodes the local level structure in the form of trends for color, shape and texture information of medical images. Experimental results demonstrate thatt the fusion of MTSD and FSVM significantly increases the retrieval precision for heterogeneeous medical image dataset. The simplest Manhattan diistance is incorporated for measuring the similarity. The feasibility of thee proposed system is extensively experimented on benchmark daataset and the experimental study clearly demonstrated that proposed fusion of MTSD with Fuzzy SVM gives significantly superior average retrieval precision.

scholarly journals Deep triplet hashing network for case-based medical image retrieval

2021 ◽  
Vol 69 ◽  
pp. 101981
Author(s):  
Jiansheng Fang ◽  
Huazhu Fu ◽  
Jiang Liu
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Case Based
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