Abstract
AbstractCase retrieval constitutes an interesting area of research which contributes to the evolution of several domains. The similarity measure module is a fundamental step in the retrieval process which affects remarkably on a retrieval system. In this context, we suggest in this paper a similarity measure applied to brain tumor cases retrieval. The rationale behind the proposed measure consists in quantifying the diagnosis correspondence followed by a clinician while comparing two medical cases. Our idea is characterized by the use of the Bayesian inference in the formulation of the proposed measure. The Bayesian network is applied in the classification task and it describes the decision-making process of a radiologist facing a tumor. The proposed similarity algorithm is based essentially on graph correspondence based on signature nodes comparison from the Bayesian classifiers. experiments were directed to compare the performance of the proposed similarity measure method with classical methods of simil...
Highlights
Information retrieval is an approach based on artificial intelligence (AI) techniques which is designed to facilitate the research of documents in complex databases[1]
We have proposed a similarity measure algorithm applied to a MRI brain tumors cases retrieval contribution
The decision concerning a brain tumor interpretation process is complicated and it is deduced from several information sources
Summary
Information retrieval is an approach based on artificial intelligence (AI) techniques which is designed to facilitate the research of documents in complex databases[1]. We propose a similarity measure approach for medical cases retrieval application. The approach is applied on a medical problem of brain tumors retrieval application. Most of the current approaches retrieve a case by proposing a classical distance between the descriptors of the tested cases These classic measures do not reflect the assimilation between two objects in a real context. The current approach relies on this observation to propose a similarity measure based on the comparison of the. The assessment of similarity between two medical cases is characterized by a probabilistic aspect These points are embodied in this paper while referring to the principles of Bayesian inference. A Bayesian network is used to classify a medical case into a brain tumor class.
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