Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Detection of colorectal cancer using dilated convolutional network via Raman spectra Description An account of the resource Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection plays a crucial role in improving patient outcomes and reducing mortality rates. In recent years, Raman spectroscopy has emerged as a promising tool for non-invasive cancer detection. This research introduces a new method for identifying colorectal cancer (CRC). It combines Raman spectroscopy, a technique that analyzes the molecular fingerprint of tissues, with a powerful deep learning algorithm called a dilated convolutional network (DCN). By combining these two tools, the researchers aim to improve the accuracy and reliability of diagnosing CRC. Intraoperative diagnostics and pathology need to distinguish tumors from normal tissues. This proposal explores Raman spectroscopy as a new surgical tool for identifying colorectal cancer during surgery. Raman spectroscopy offers a way to directly analyze the makeup of tissue, potentially revealing the presence of cancer. However, surrounding tissue can create background interference, making it difficult to detect the key signal. The authors suggest that high-quality data from Raman spectroscopy combined with advanced deep learning algorithms could be a solution to overcome this challenge. We collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with Raman shifts from 385 to 1545 cm-1. Second, dilated convolutional networks classify colorectal cancer tumour tissues. Following the deep learning model's output, we proceed by visualizing and analyzing the identified fingerprint peaks. Our deep learning algorithm exceeds previous colorectal cancer detection methods with 99.1% accuracy. Colorectal cancer detection using Raman spectra is unique. Our ensemble DCN could classify colorectal tumour and normal tissue Raman spectra. 2024 Author(s). Creator An entity primarily responsible for making the resource Tanwar S.; Sivakumar V.; Nataraj C.; Vijaylakshmi S. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-3161, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0229492" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0229492</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203974247&amp;doi=10.1063%2F5.0229492&amp;partnerID=40&amp;md5=7d410e9b1f3ef13ab84367ecd012ea3d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203974247&amp;doi=10.1063%2f5.0229492&amp;partnerID=40&amp;md5=7d410e9b1f3ef13ab84367ecd012ea3d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tanwar S., Manipal University, Jaipur, India; Sivakumar V., Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; Nataraj C., Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; Vijaylakshmi S., Christ (Deemed to Be University), Pune, India