Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques
Colorectal cancer is among the most widespread cancers globally, and the risk of developing this disease increases with age. This has led to the recommendation that screening should begin in middle-aged patients. Consequently, the implementation of prevention programs has resulted in a greater numbe...
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IEEE
2025-01-01
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| author | Jakub Tomes Daniela Janstova Shayestegan Mohsen Alla Sinica Zuzana Kovacova Jaromir Petrtyl Jan Mares |
| author_facet | Jakub Tomes Daniela Janstova Shayestegan Mohsen Alla Sinica Zuzana Kovacova Jaromir Petrtyl Jan Mares |
| author_sort | Jakub Tomes |
| collection | DOAJ |
| description | Colorectal cancer is among the most widespread cancers globally, and the risk of developing this disease increases with age. This has led to the recommendation that screening should begin in middle-aged patients. Consequently, the implementation of prevention programs has resulted in a greater number of samples being available for histological analysis. Raman spectroscopy offers a diagnostic solution to this challenge. In this study, we present a rapid procedure for Raman tissue analysis and classification using machine learning methods, including both supervised and hybrid supervised-unsupervised approaches, to distinguish between healthy and pathological cases based on Raman spectroscopy data. Raman spectra were acquired using a handheld portable spectrometer with an automatic BubbleFill preprocessing algorithm, covering at spectral range of 500 to 1800 cm-1. Various machine-learning algorithms have been evaluated for classification, including Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), and hybrid supervised-unsupervised methods involving dimensionality reduction, clustering, and classification. The experimental results indicated promising classification accuracy, achieving up to 82% accuracy using the MLP algorithm. This suggests the potential effectiveness of neural network methodologies for the classification of Raman spectroscopy data for the diagnosis of colorectal cancer. |
| format | Article |
| id | doaj-art-8d5f55b2ed50431f82d4b2e9005e3d73 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8d5f55b2ed50431f82d4b2e9005e3d732025-08-20T03:11:48ZengIEEEIEEE Access2169-35362025-01-0113296012961210.1109/ACCESS.2025.353918110872948Rapid Colorectal Tissue Classification Using Data-Driven Raman TechniquesJakub Tomes0https://orcid.org/0000-0002-1643-6250Daniela Janstova1https://orcid.org/0000-0002-0300-3878Shayestegan Mohsen2https://orcid.org/0000-0002-2709-6503Alla Sinica3Zuzana Kovacova4https://orcid.org/0009-0009-7853-724XJaromir Petrtyl5Jan Mares6https://orcid.org/0000-0003-4693-2519Department of Mathematics, Informatics and Cybernetics, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Prague, Czech RepublicDepartment of Mathematics, Informatics and Cybernetics, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Prague, Czech RepublicFaculty of Electrical Engineering and Informatics, University of Pardubice, Pardubice, Czech RepublicDepartment of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Prague, Czech RepublicDepartment of Analytical Chemistry, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Prague, Czech Republic4th Internal Clinic-Gastroenterology and Hepatology, First Faculty of Medicine, Charles University, Prague, Czech RepublicDepartment of Mathematics, Informatics and Cybernetics, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Prague, Czech RepublicColorectal cancer is among the most widespread cancers globally, and the risk of developing this disease increases with age. This has led to the recommendation that screening should begin in middle-aged patients. Consequently, the implementation of prevention programs has resulted in a greater number of samples being available for histological analysis. Raman spectroscopy offers a diagnostic solution to this challenge. In this study, we present a rapid procedure for Raman tissue analysis and classification using machine learning methods, including both supervised and hybrid supervised-unsupervised approaches, to distinguish between healthy and pathological cases based on Raman spectroscopy data. Raman spectra were acquired using a handheld portable spectrometer with an automatic BubbleFill preprocessing algorithm, covering at spectral range of 500 to 1800 cm-1. Various machine-learning algorithms have been evaluated for classification, including Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), and hybrid supervised-unsupervised methods involving dimensionality reduction, clustering, and classification. The experimental results indicated promising classification accuracy, achieving up to 82% accuracy using the MLP algorithm. This suggests the potential effectiveness of neural network methodologies for the classification of Raman spectroscopy data for the diagnosis of colorectal cancer.https://ieeexplore.ieee.org/document/10872948/Classification algorithmsclinical diagnosismachine learningsupervised learningspectroscopy |
| spellingShingle | Jakub Tomes Daniela Janstova Shayestegan Mohsen Alla Sinica Zuzana Kovacova Jaromir Petrtyl Jan Mares Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques IEEE Access Classification algorithms clinical diagnosis machine learning supervised learning spectroscopy |
| title | Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques |
| title_full | Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques |
| title_fullStr | Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques |
| title_full_unstemmed | Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques |
| title_short | Rapid Colorectal Tissue Classification Using Data-Driven Raman Techniques |
| title_sort | rapid colorectal tissue classification using data driven raman techniques |
| topic | Classification algorithms clinical diagnosis machine learning supervised learning spectroscopy |
| url | https://ieeexplore.ieee.org/document/10872948/ |
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