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An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application

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Abstract

Content Based Image Retrieval (CBIR) is a popular method to search and retrieve the similar images. For medical applications, it plays an important role to find the diseasessuch as breast cancer in human body. Many existing methods were presented for improving the performance of CBIR method. Nevertheless, retrieval time and accuracy of CBIR are further to be improved. To solve this issue, an optimal classifier is to be used in CBIR. In this paper, Artificial Neural Network based on Particle Swarm Optimization based (PSO-ANN) is presented as an optimized classifier. Also, the features of images such as shape, texture, mean and standard deviation are extracted. To increase the speed of the classification, these extracted features are to be clustered using k-means clustering algorithm. From the clustered features, similar images of query image are retrieved using the proposed PSO-ANN classifier. Simulation results prove that performance of this proposed CBIR outperforms than that of existing methods in terms of accuracy and CBIR time.

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Authors and Affiliations

  1. Pentagram Research Centre Pvt. Ltd, Hyderabad, India

    R. Biji Kumar

  2. Department of Information Technology, Anna University, Chennai, India

    P. Marikkannu

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  1. R. Biji Kumar
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  2. P. Marikkannu
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Correspondence to R. Biji Kumar.

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Kumar, R.B., Marikkannu, P. An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application. Multimed Tools Appl 79, 22277–22292 (2020). https://doi.org/10.1007/s11042-020-08953-z

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  • DOI: https://doi.org/10.1007/s11042-020-08953-z

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