An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

T Anitha; T Kalaiselvi

doi:10.17485/IJST/v17sp1.283

Article

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17sp1.283

Year: 2024, Volume: 17, Issue: Special Issue 1, Pages: 40-44

Original Article

An Investigation of Distance Measures for Development of Effective Content Based Tumor Image Retrieval System

T Anitha¹, T Kalaiselvi^1*

¹Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, 624 302, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:29 August 2023, Accepted Date:05 March 2024, Published Date:25 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: The MRI has proven to be extremely effective in detecting tumors, with millions of images created each day throughout the world. To find similar images from a vast collection, Content-Based Tumor Image Retrieval (CBTIR) technology has been used to analysis the medical image. In the traditional retrieval methods, retrieving a similar image from the large database is crucial task. To overcome this issue we developed deep learning based retrieval method. Methods: This research offers a retrieval approach based on predefined ResNet models for quick and accurate image retrieval. We tested various prominent ResNet models with different distance similarity metrics, and the best option was determined by this work. Findings: After the various evaluation of ResNet models with varied distance measures on the CE-MRI data set, ResNet50 model applied with Hamming distance yields 99.33% of retrieval precision. Novelty: This work used predefined ResNet models with the combination of Distance similarity metrics to achieve more accurate results on medical image retrieval compared to the other conventional methods.

Keywords: Content Based Image Retrieval, Tumor Retrieval, Hamming Distance, Euclidean Distance, Minkowski

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Copyright

© 2024 Anitha & Kalaiselvi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)