International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 12 (December 2022), Pages: 11-24

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 Original Research Paper

 Pre-trained convolution neural networks models for content-based medical image retrieval

 Author(s): Ali Ahmed 1, *, Alaa Omran Almagrabi 2, Ahmed Hamza Osman 2

 Affiliation(s):

 1Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia
 2Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-8944-8922

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.12.002

 Abstract:

Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Pre-trained convolution neural networks, Features extraction, Medical image retrieval

 Article History: Received 20 January 2022, Received in revised form 15 May 2022, Accepted 16 August 2022

 Acknowledgment 

The authors gratefully acknowledge the Deanship of Scientific Research‎ ‎‎(DSR) at King Abdulaziz University‎ for their technical and financial ‎support. ‎ This research was funded by the Deanship of Scientific Research (DSR) at ‎King Abdulaziz University, Jeddah, Saudi Arabia, under ‎Grant No.‎ (G: ‎‎146-830-1441‎093-830-1441).

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Ahmed A, Almagrabi AO, and Osman AH (2022). Pre-trained convolution neural networks models for content-based medical image retrieval. International Journal of Advanced and Applied Sciences, 9(12): 11-24

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 

 Tables

 Table 1 Table 2 Table 3 

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