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Skin Cancer Detection Using Ant Colony Optimization Based Content-Based Image Retrieval System

International Journal of Electronics and Communication Engineering
© 2024 by SSRG - IJECE Journal
Volume 11 Issue 8
Year of Publication : 2024
Authors : Layak Ali, A. Santhosha Kumar
10.14445/23488549/IJECE-V11I8P106
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How to Cite?

Layak Ali, A. Santhosha Kumar, "Skin Cancer Detection Using Ant Colony Optimization Based Content-Based Image Retrieval System," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 8, pp. 55-61, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I8P106

Abstract:

“Ant Colony Optimization” (ACO) is an optimization technique developed from the social behaviour of ant colonies. The ACO is being implemented and used in many applications, including image processing. One of the recent developments in image processing is “Content-Based Image Retrieval” (CBIR). The “CBIR” method looks for similar images in the big dataset of images based on image content. This paper effectively integrates ACO with CBIR, and the proposed system is called ACOCBIR. The proposed system is first tested on general images for its suitability. Further, it is used for detecting major skin cancerous images from large image datasets. It is found that ACO-CBIR performs better compared to basic strategies from state of the art and it detects skin cancer effectively.

Keywords:

ACO, CBIR, Benign, Malignant, Skin cancer.

References:

[1] Marco Dorigo, and Thomas Stützle, Ant Colony Optimization, MIT Press, 2004.
[CrossRef] [Publisher Link]
[2] Alberto Colorni, Marco Dorigo, and Vittorio Maniezzo, “Distributed Optimization by Ant Colonies,” Proceedings of ECAL91 - European Conference on Artificial Life, Paris, France, Elsevier Publishing, pp. 134-142, 1991.
[Google Scholar] [Publisher Link]
[3] Eric Bonabeau, Marco Dorigo, and Guy Theraulaz, Swarm Intelligence From Natural to Artificial Systems, Oxford University Press, pp. 1-322. 1999.
[Google Scholar] [Publisher Link]
[4] E. Bonabeau, M. Dorigo, and G. Theraulaz, “Inspiration for Optimization from Social Insect Behaviour,” Nature, vol. 406, pp. 39-42, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[5] John Eakins, and Margaret Graham, “Content-Based Image Retrieval,” Institute for Image Data Research, University of Northumbria at Newcastle, pp. 1-66, 1999.
[Google Scholar] [Publisher Link]
[6] Toshikazu Kato, “Database Architecture for Content-Based Image Retrieval,” Image Storage and Retrieval Systems, vol. 1662, pp. 112- 123, 1992.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Michael S. Lew et al., “Content-Based Multimedia Information Retrieval: State of the Art and Challenges,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 2, no. 1, pp. 1-19, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Zhongyu Li et al., “Large-Scale Retrieval for Medical Image Analytics: A Comprehensive Review,” Medical Image Analysis, vol. 43, pp. 66-84, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Han Liu, Wenqing Wang, and Pengfei Jiao, “Content Based Image Retrieval via Sparse Representation and Feature Fusion,” 2019 IEEE 8 th Data Driven Control and Learning Systems Conference, Dali, China, pp. 18-23, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Niveditha Arunkumar, and A. Ranjith Ram, “CBIR Systems: Techniques and Challenges,” 2020 International Conference on Communication and Signal Processing, Chennai, India, pp. 141-146, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Remco C. Veltkamp, Mirela Tanase, and Danielle Sent, “Features in Content-Based Image Retrieval Systems: A Survey,” State-of-theArt in Content-Based Image and Video Retrieval, Computational Imaging and Vision, vol. 22, pp. 97-124, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Ni Zhang et al., “Skin Cancer Diagnosis Based on Optimized Convolutional Neural Network,” Artificial Intelligence in Medicine, vol. 102, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jia Li, and J.Z. Wang, “Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[14] J.Z. Wang, Jia Li, and G. Wiederhold, “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Datasets, Kaggle. [Online]. Available: https://www.kaggle.com/datasets/
[16] Nandini Nayar et al., “Ant Colony Optimization: A Review of Literature and Application in Feature Selection,” Inventive Computation and Information Technologies, Lecture Notes in Networks and Systems, vol. 173, pp. 285-297, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] B. Chandra Mohan, and R. Baskaran, “A Survey: Ant Colony Optimization Based Recent Research and Implementation on Several Engineering Domain,” Expert Systems with Applications, vol. 34, no. 4, pp. 4618-4627, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Sana Nazari, and Rafael Garcia, “Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review,” Life, vol. 13, no. 11, pp. 1-33, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Faruk Alendar et al., “Early Detection of Melanoma Skin Cancer,” Bosnian Journal of Basic Medical Sciences, vol. 9, no. 1, pp. 77-80, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[20] The Sun & Your Skin: What You Need to Know, Skin Cancer. [Online]. Available: https://www.skincancer.org/
[21] Atheer Bassel et al., “Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach,” Diagnostics, vol. 12, no. 10, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Pradnya A. Vikhar, and P.P. Karde, “Content Based Image Retrieval (CBIR) System Using Threshold Based Color Layout Descriptor (CLD) and Edge Histogram Descriptor (EHD),” International Journal of Computer Applications, vol. 179, no. 41, pp. 39-43, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Md. Farhan Sadique, and S.M. Rafizul Haque, “Content-Based Image Retrieval Using Color Layout Descriptor, Gray-Level Co-Occurrence Matrix and K-Nearest Neighbors,” International Journal of Information Technology and Computer Science, vol. 12, no. 3, pp. 19-25, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Chuen-Horng Lin et al., “Fast K-Means Algorithm Based on a Level Histogram for Image Retrieval,” Expert Systems with Applications, vol. 41, no. 7, pp. 3276-3283, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[25] S. Muhammad Hossein Mousavi, ACO Image Feature Extraction, Mathworks. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/104385-aco-image-feature-extraction
[26] William Hsu, L. Rodney Long, and Sameer Antani, “Spirs: A Framework for Content-Based Image Retrieval from Large Biomedical Databases,” Studies in Health Technology and Informatics, vol. 129, pp. 188-192, 2007.
[Google Scholar] [Publisher Link]
27] Lin Yang et al., “PathMiner: A Web-Based Tool for Computer-Assisted Diagnostics in Pathology,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 3, pp. 291-299, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Lei Zheng et al., “Design and Analysis of a Content-Based Pathology Image Retrieval System,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 4, pp. 249-255, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Hatice Cinar Akakin, and Metin N. Gurcan, “Content-Based Microscopic Image Retrieval System for Multi-Image Queries,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 4, pp. 758-769, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Henning Müller et al., “A Review of Content-Based Image Retrieval Systems in Medical Applications-Clinical Benefits and Future Directions,” International Journal of Medical Informatics, vol. 73, no. 1, pp. 1-23, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Yuheng Wang et al., “Multi-Channel Content Based Image Retrieval Method for Skin Diseases Using Similarity Network Fusion and Deep Community Analysis,” Biomedical Signal Processing and Control, vol. 78, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Stefano Allegretti et al., “Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval,” 2020 25th International Conference on Pattern Recognition, Milan, Italy, pp. 8053-8060, 2021.
[CrossRef] [Google Scholar] [Publisher Link]