Rapid Colorectal Tissue Classification Using DataDriven Raman Techniques

Project Code :TCMAPY1559

Objective

This study proposes a rapid colorectal tissue classification system leveraging Raman spectroscopy and machine learning to distinguish between normal and malignant tissues. Raman spectroscopy provides molecular-level insights into tissue composition, while advanced algorithms such as K-best feature selection, Decision Tree, and CatBoost enable efficient and interpretable classification. The system utilizes a labeled dataset from Kaggle, applies PCA for dimensionality reduction, and selects key spectral features for classification.

Abstract

Colorectal cancer (CRC) is among the leading causes of cancer-related deaths worldwide, emphasizing the need for early, accurate, and non-invasive diagnostic methods. This study proposes a rapid colorectal tissue classification system leveraging Raman spectroscopy and machine learning to distinguish between normal and malignant tissues. Raman spectroscopy provides molecular-level insights into tissue composition, while advanced algorithms such as K-best feature selection, Decision Tree, and CatBoost enable efficient and interpretable classification. The system utilizes a labeled dataset from Kaggle, applies PCA for dimensionality reduction, and selects key spectral features for classification. Unlike traditional invasive diagnostics, this approach offers a faster, real-time, and accurate alternative for CRC detection. The integration of CatBoost, known for handling categorical and noisy data, further enhances model performance. The proposed model demonstrates high accuracy and robustness, offering a clinically relevant, non-invasive solution for early CRC detection.

Keywords: Colorectal Cancer, Raman Spectroscopy, Machine Learning, K-best, PCA, Decision Tree, CatBoost, Tissue Classification, Non-Invasive Diagnosis.

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