A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra
Abstract
Objective and Impact Statement. Distinguishing tumors from normal tissues is vital in intraoperative diagnosis and pathological examination. In this work, we propose to utilize Raman spectroscopy as a novel modality in surgery to detect colorectal cancer tissues. Introduction. Raman spectra can reflect the substance components of the target tissues. However, the feature peak is slight and hard to detect due to environmental noise. Collecting a high-quality Raman spectroscopy dataset and developing effective deep learning detection methods are possibly viable approaches. Methods. First, we collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with the Raman shift ranged from 385 to 1545 cm^{−1}^ . Second, a one-dimensional residual convolutional neural network (1D-ResNet) architecture is designed to classify the tumor tissues of colorectal cancer. Third, we visualize and interpret the fingerprint peaks found by our deep learning model. Results. Experimental results show that our deep learning method achieves 98.5% accuracy in detection of colorectal cancer, and outperforms traditional methods. Conclusion. Overall, our proposed method could become a promising tool for clinical detection of colorectal cancer.
