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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Medical case retrieval from a committee of decision trees
Gwénolé Quellec1, Mathieu Lamard, Lynda Bekri
1INSTITUT TELECOM/TELECOM Bretagne, Dpt ITI, Brest, F-29200 France. gwenole.quellec@telecom-bretagne.eu
Abstract:
A novel content-based information retrieval framework, designed to cover several medical applications, is presented in this paper. The presented framework allows the retrieval of possibly incomplete medical cases consisting of several images together with semantic information. It relies on a committee of decision trees, decision support tools well suited to process this type of information. In our proposed framework, images are characterized by their digital content. It was applied to two heterogeneous medical datasets for computer-aided diagnoses: a diabetic retinopathy follow-up dataset (DRD) and a mammography-screening dataset (DDSM). Measure of precision among the top five retrieved results of 0.788 + or - 0.137 and 0.869 + or - 0.161 was obtained on DRD and DDSM, respectively. On DRD, for instance, it increases by half the retrieval of single images.
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