Open AccessFluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms
Author(s) -
Linwei Shang,
Dan-Ying Ma,
Juanjuan Fu,
Yanfei Lu,
Yuan Zhao,
Xianghong Xu,
Jianhua Yin
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.394772
Subject(s) - artificial intelligence , raman spectroscopy , linear discriminant analysis , computer science , breast cancer , pattern recognition (psychology) , convolutional neural network , partial least squares regression , artificial neural network , hyperspectral imaging , algorithm , optics , cancer , physics , machine learning , medicine
Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.