Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms

Abstract

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.

Fig. 1.

The schematic diagram of excitation and collection light paths in home-made micro Raman spectrometer.

Fig. 2.

(a) The steps and results of the pseudo-color enhancement and (b) the modified parts of the GoogLeNet (contained in the dotted line).

Fig. 3.

(a) Discriminant accuracy curves of the training sets (blue curve) and validation sets (black scatter) in training process. (b) loss function curves of the training sets (red curve) and validation sets (black scatter) in training process. (c) The receiver operating characteristic curve of the test sets.

Fig. 4.

Visible light photo of breast tissue section (150-µm thickness) under (a) 5x and (b) 20x objective lens. The positions marked as A and B in (b) represent the extracellular matrix and the globose structures in the section, respectively. (c) Average spectra of collagen and lipid collected from the ECM and the globose structures, respectively. (d) Schematic diagrams of the structure of BP-neural networks for collagen and (e) lipid.

Fig. 5.

Mean squared error curves of (a) collagen and (b) lipid. The two curves in either graph correspond to training sets (blue curve) and validation sets (red curve), respectively. Receiver operating characteristic curve of (c) collagen and (d) lipid.

Fig. 6.

Predictive value of the breast samples with the PLS model. The dashed line represents the threshold of 1.5.