Detection of Intestinal Tumors Outside the Visibility of Capsule Endoscopy Camera Utilizing Radio Signal Recognition
Särestöniemi, Mariella; Taparugssanagorn, Attaphongse; Iinatti, Jari; Myllylä, Teemu (2024-05-05)
Springer
05.05.2024
Särestöniemi, M., Taparugssanagorn, A., Iinatti, J., Myllylä, T. (2024). Detection of Intestinal Tumors Outside the Visibility of Capsule Endoscopy Camera Utilizing Radio Signal Recognition. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2084. Springer, Cham. https://doi.org/10.1007/978-3-031-59091-7_28
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405304096
https://urn.fi/URN:NBN:fi:oulu-202405304096
Tiivistelmä
Abstract
Early cancer detection is crucial, especially for intestinal cancer with subtle early symptoms. While camera-based Wireless Capsule Endoscopy (WCE) systems are efficient, patient-friendly, and safe investigating gastrointestinal (GI) track thoroughly, some limitations persist in visualizing only the inner part of the GI regions. Our study introduces a radio channel analysis -based approach to detect intestinal/abdominal tumors which are not visible for the WCE camera, i.e., the tumors which have started to grow on the outer parts of the intestinal track. Focused on S-parameter patterns in realistic human voxel models, our simulation-based method discerns dielectric property variations in normal and tumorous tissues, replicating intricate tissue characteristics. Preliminary simulation results in different intestine locations demonstrate our technique’s efficacy in differentiating normal and tumor cases based on S-parameter patterns. With a 98% accuracy rate, simple logistic regression classification model excels in distinguishing normal from tumor tissues, significantly enhancing diagnostic precision in GI health monitoring showcasing its potential to revolutionize early cancer detection and advance diagnostic accuracy within simulated human anatomy. This represents a substantial stride toward improving healthcare outcomes through cutting-edge technology.
Early cancer detection is crucial, especially for intestinal cancer with subtle early symptoms. While camera-based Wireless Capsule Endoscopy (WCE) systems are efficient, patient-friendly, and safe investigating gastrointestinal (GI) track thoroughly, some limitations persist in visualizing only the inner part of the GI regions. Our study introduces a radio channel analysis -based approach to detect intestinal/abdominal tumors which are not visible for the WCE camera, i.e., the tumors which have started to grow on the outer parts of the intestinal track. Focused on S-parameter patterns in realistic human voxel models, our simulation-based method discerns dielectric property variations in normal and tumorous tissues, replicating intricate tissue characteristics. Preliminary simulation results in different intestine locations demonstrate our technique’s efficacy in differentiating normal and tumor cases based on S-parameter patterns. With a 98% accuracy rate, simple logistic regression classification model excels in distinguishing normal from tumor tissues, significantly enhancing diagnostic precision in GI health monitoring showcasing its potential to revolutionize early cancer detection and advance diagnostic accuracy within simulated human anatomy. This represents a substantial stride toward improving healthcare outcomes through cutting-edge technology.
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Ellei muuten mainita, aineiston lisenssi on https://creativecommons.org/licenses/by/4.0/