{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T06:39:46Z","timestamp":1768891186911,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T00:00:00Z","timestamp":1700784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National and Kapodistrian University of Athens"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.<\/jats:p>","DOI":"10.3390\/jimaging9120261","type":"journal-article","created":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T11:19:46Z","timestamp":1700824786000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2186-4567","authenticated-orcid":false,"given":"Dimitris","family":"Kalatzis","sequence":"first","affiliation":[{"name":"2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7270-4387","authenticated-orcid":false,"given":"Ellas","family":"Spyratou","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"},{"name":"Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6227-3272","authenticated-orcid":false,"given":"Maria","family":"Karnachoriti","sequence":"additional","affiliation":[{"name":"Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece"},{"name":"School of Applied Mathematical and Physical Sciences, National Technical University Athens, 15780 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3296-3317","authenticated-orcid":false,"given":"Maria Anthi","family":"Kouri","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"},{"name":"Medical Physics Program, University of Massachusetts Lowell, Lowell, MA 01854, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7769-7534","authenticated-orcid":false,"given":"Ioannis","family":"Stathopoulos","sequence":"additional","affiliation":[{"name":"2nd Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolaos","family":"Danias","sequence":"additional","affiliation":[{"name":"4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 12462 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolaos","family":"Arkadopoulos","sequence":"additional","affiliation":[{"name":"4th Department of Surgery, School of Medicine, Attikon University Hospital, University of Athens, 12462 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9763-5560","authenticated-orcid":false,"given":"Spyros","family":"Orfanoudakis","sequence":"additional","affiliation":[{"name":"Alpha Information Technology S.A., Software & System Development, 68131 Alexandroupolis, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-5271","authenticated-orcid":false,"given":"Ioannis","family":"Seimenis","sequence":"additional","affiliation":[{"name":"Medical School, National and Kapodistrian University of Athens, 11527 Athens, 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guidance: Translation to the clinics","volume":"142","author":"Santos","year":"2017","journal-title":"Analyst"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.copbio.2009.02.006","article-title":"Chemical analysis in vivo and in vitro by Raman spectroscopy from single cells to humans","volume":"20","author":"Weeks","year":"2009","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kouri, M.A., Spyratou, E., Karnachoriti, M., Kalatzis, D., Danias, N., Arkadopoulos, N., Seimenis, I., Raptis, Y.S., Kontos, A.G., and Efstathopoulos, E.P. (2022). Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians\u2019 Hands for In Situ Cancer Diagnosis and Surgery Guidance. Cancers, 14.","DOI":"10.3390\/cancers14051144"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1067\/mge.2003.105","article-title":"Diagnostic potential of near-infrared Raman spectroscopy in the colon: Differentiating adenomatous from hyperplastic polyps","volume":"57","author":"Molckovsky","year":"2003","journal-title":"Gastrointest. Endosc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1089\/pho.2006.2066","article-title":"Discrimination of normal and malignant mucosal tissues of the colon by Raman spectroscopy","volume":"25","author":"Chowdary","year":"2007","journal-title":"Photomed. Laser Surg."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1021\/ac503287u","article-title":"Characterizing Variability of In Vivo Raman Spectroscopic Properties of Different Anatomical Sites of Normal Colorectal Tissue towards Cancer Diagnosis at Colonoscopy","volume":"87","author":"Bergholt","year":"2015","journal-title":"Anal. Chem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3249","DOI":"10.1039\/c3ee42282h","article-title":"Depleted hole conductor-free lead halide iodide heterojunction solar cells","volume":"6","author":"Laban","year":"2013","journal-title":"Energy Environ. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6763","DOI":"10.1364\/BOE.476507","article-title":"Impact of preprocessing methods on the Raman spectra of brain tissue","volume":"13","author":"Wahl","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"ref_9","first-page":"20170043","article-title":"Analyzing Raman spectroscopic data","volume":"4","author":"Ryabchykov","year":"2018","journal-title":"Phys. Sci. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1002\/jrs.5608","article-title":"Optimized preprocessing and machine learning for quantitative Raman spectroscopy in biology","volume":"50","author":"Storey","year":"2019","journal-title":"J. Raman Spectrosc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1177\/0003702819888949","article-title":"Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks","volume":"74","author":"Wahl","year":"2020","journal-title":"Applied Spectroscopy. Appl. Spectrosc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat. Methods"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1038\/s41416-021-01659-5","article-title":"Raman spectroscopy: Current applications in breast cancer diagnosis, challenges and future prospects","volume":"126","author":"Hanna","year":"2022","journal-title":"Br. J. Cancer"},{"key":"ref_14","first-page":"653","article-title":"Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines","volume":"32","author":"Widjaja","year":"2008","journal-title":"Int. J. Oncol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Riva, M., Sciortino, T., Secoli, R., D\u2019Amico, E., Moccia, S., Fernandes, B., Conti Nibali, M., Gay, L., Rossi, M., and De Momi, E. (2021). Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples. Cancers, 13.","DOI":"10.3390\/cancers13051073"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ma, D., Shang, L., Tang, J., Bao, Y., Fu, J., and Yin, J. (2021). Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network. Spectrochim. Acta A Mol. Biomol. Spectrosc., 256.","DOI":"10.1016\/j.saa.2021.119732"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Karnachoriti, M., Stathopoulos, I., Kouri, M., Spyratou, E., Orfanoudakis, S., Lykidis, D., Lambropoulou, \u039c., Danias, N., Arkadopoulos, N., and Efstathopoulos, E.P. (2023). Biochemical differentiation between cancerous and normal human colorectal tissues by micro-Raman spectroscopy. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 299.","DOI":"10.1016\/j.saa.2023.122852"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"310","DOI":"10.3390\/opt4020022","article-title":"Advanced Raman Spectroscopy Based on Transfer Learning by Using a Convolutional Neural Network for Personalized Colorectal Cancer Diagnosis","volume":"4","author":"Kalatzis","year":"2023","journal-title":"Optics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1093\/nar\/gkm433","article-title":"Novel zinc-based fixative for high quality DNA, RNA and protein analysis","volume":"35","author":"Lykidis","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1038\/nprot.2016.036","article-title":"Using Raman spectroscopy to characterize biological materials","volume":"11","author":"Butler","year":"2016","journal-title":"Nat. Protoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/0168-583X(88)90063-8","article-title":"SNIP, a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications","volume":"34","author":"Ryan","year":"1988","journal-title":"Nucl. Instrum. Methods Phys. Res. B"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11424-022-1326-y","article-title":"Improve Robustness and Accuracy of Deep Neural Network with L2,\u221e Normalization","volume":"36","author":"Yu","year":"2023","journal-title":"J. Syst. Sci. Complex."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","article-title":"Principal components analysis (PCA)","volume":"19","author":"Ratajczak","year":"1993","journal-title":"Comput. Geosci."},{"key":"ref_24","unstructured":"(2022, October 15). Scikit-Learn. Available online: https:\/\/scikit-learn.org\/stable."},{"key":"ref_25","unstructured":"(2022, October 15). Keras. Available online: https:\/\/keras.io\/api\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","first-page":"336","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"2","author":"Selvaraju","year":"2019","journal-title":"Int. J. Comput. Vision"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/12\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:29:50Z","timestamp":1760131790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/9\/12\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,24]]},"references-count":27,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["jimaging9120261"],"URL":"https:\/\/doi.org\/10.3390\/jimaging9120261","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,24]]}}}