Abstract
In this work, an automated system is designed to identify and classify the modality of medical images. We considered six modalities in this work: X-ray (XR), computed tomography (CT), magnetic resonance imaging (MR), positron emission tomography (PET), ultrasound (US) and photographs (PX). The methodology is based on encoding scale invariant feature transform (SIFT) features using Bag of Visual Words (BoVW), vector of locally aggregated descriptors (VLAD) and Fisher vector (FV). The encoded features are fed to support vector machine (SVM) classifier for training. The classification accuracy of all the classifiers based on three encoding strategies is compared and analyzed. The hybrid model is then implemented by selecting the best performance from each case. The major contribution of this research work is the application of VLAD for modality classification task which has not been tried so far. Combining the best performance of three encoding strategies, the overall classification accuracy obtained with the proposed system is 90.7%. For identification task, the scores from all the three encoding strategies are combined and the recognition rate obtained is 77.7%.
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14 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04336-4
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Sundarambal, B., Subramanian, S. & Muthukumar, B. RETRACTED ARTICLE: A hybrid encoding strategy for classification of medical imaging modalities. J Ambient Intell Human Comput 12, 5853–5863 (2021). https://doi.org/10.1007/s12652-020-02129-1
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DOI: https://doi.org/10.1007/s12652-020-02129-1