Statistical analysis and machine learning algorithms for optical biopsy

Binlin Wu; Cheng-hui Liu; Susie Boydston-White; Hugh Beckman; Vidyasagar Sriramoju; Laura Sordillo; Chunyuan Zhang; Lin Zhang; Lingyan Shi; Jason Smith; Jacob Bailin; Robert R. Alfano

doi:10.1117/12.2288089

19 February 2018 Statistical analysis and machine learning algorithms for optical biopsy

Binlin Wu, Cheng-hui Liu, Susie Boydston-White, Hugh Beckman, Vidyasagar Sriramoju, Laura Sordillo, Chunyuan Zhang, Lin Zhang, Lingyan Shi, Jason Smith, Jacob Bailin, Robert R. Alfano

Author Affiliations +

Proceedings Volume 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis; 104890T (2018) https://doi.org/10.1117/12.2288089
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

Citation Download Citation

Binlin Wu, Cheng-hui Liu, Susie Boydston-White, Hugh Beckman, Vidyasagar Sriramoju, Laura Sordillo, Chunyuan Zhang, Lin Zhang, Lingyan Shi, Jason Smith, Jacob Bailin, and Robert R. Alfano "Statistical analysis and machine learning algorithms for optical biopsy", Proc. SPIE 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 104890T (19 February 2018); https://doi.org/10.1117/12.2288089

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