Supporting content based visual information retrieval for medical imaging with lenient relevance feedback

Nashwan Jasim Hussein

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The objective of CBIR is to retrieve relevant medical images from the medical database with reference to the query image in a shorter span of time. All the proposed approaches are different, yet the research goal is to attain better accuracy in a reasonable amount of time. The initial phase of this research presents a feature selection technique that aims to improvise the medical image diagnosis by selecting prominent features. The second phase of the research extracts features and the association rules are formed by the proposed Classification Based on Highly Strong Association Rules (CHiSAR). Finally, the rule subset classifier is employed to classify between the images. The final phase of the research extracts the features from the kidney images and the association rules are reduced for better performance. The image relevance inference is performed and finally, binary and the best first search classification is employed to classify between the images. The system was effectively prepared for 10 classes of CT scan image and in the proposed IICB Merge FS based CNN classifier provide high accuracy of 97.24% is obtained by comparing with the state of arts in MATLAB software. Keywords: 1-Content Based Information Interval in Consistency Based Merge 2- Feature Selection 3- Feature extraction 4- Classification 5- Image analysis

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