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Deep features based medical image retrieval

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Abstract

In this paper, deep convolutional neural network model is developed for classification and image retrieval purpose. Deep learning techniques obtained deep features from different abstraction levels represented using multiple hidden layers. This ultimately improves retrieval performance. We developed a framework based on multilayered convolutional neural network to represent medical images as deep features which are used for retrieval purpose. The proposed network’s architecture is similar to Alexnet. The extracted deep features are used to calculate similarity index between the images using distance measures and based on similarity index, retrieval is done. By using a data augmentation technique the model achieved better retrieval accuracy. The model is evaluated on different medical databases using evaluation criteria like average retrieval precision and average retrieval recall and results are compared with the state of the art image retrieval techniques. From comparison retrieval results are significantly improved.

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, SGGSIET, Nanded, Maharashtra, India

    Nilima B. Mohite & Anil B. Gonde

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  1. Nilima B. Mohite
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  2. Anil B. Gonde
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Correspondence to Nilima B. Mohite.

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We here all declare no potential conflict of interest. This statement is to certify that all authors have seen and approved the manuscript being submitted. We warrant that the article has not received prior publication and is not under consideration for publication elsewhere.

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Mohite, N.B., Gonde, A.B. Deep features based medical image retrieval. Multimed Tools Appl 81, 11379–11392 (2022). https://doi.org/10.1007/s11042-022-12085-x

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  • DOI: https://doi.org/10.1007/s11042-022-12085-x

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