Resonance raman of BCC and normal skin - Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; M.d, Hugh Beckman; Alfano, Robert R. (Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2017)
The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.
7 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/015-837-955-575-755, pop00046 tex.type: [object Object]
Keywords: Basal cell carcinoma; Basel cell carcinoma (BCC); Biopsy; Cell carcinoma; Diagnostic algorithms; Histology; Human skin; Human skin tissues; Laser excitation; Principal component analysis; Principal component analysis (PCA); Resonance; Resonance Raman spectra; Resonance Raman spectroscopy; Skin cancer; Skin cancers; Spectral differences; Statistical methods; Support vector machine (SVM); Support vector machines; Tissue; Tissue engineering; dept.phy

Statistical analysis and machine learning algorithms for optical biopsy - Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
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.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/030-448-994-607-54X, pop00047
Keywords: Artificial neural network; Biological basis; Biopsy; Cancer diagnosis; Cancerous tissues; Factorization; Learning algorithms; Learning systems; Least squares approximations; Machine learning; Matrix algebra; Neural networks; Nonnegative matrix factorization; Optical biopsies; Optical biopsy; Partial least square (PLS); Partial least squares; Principal component analysis; Resonance Raman spectroscopy; Spatial signature; Support vector machine; Support vector machines; Tissue; Tissue engineering; dept.phy