Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning

Abstract

Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recognition of different breast tissues. In parallel, the one-dimensional convolutional neural network (1D-CNN) stands out in the field of deep learning for its ability to efficiently process sequential data, such as spectroscopic signals. In this study, MIR spectra of breast tissue were collected in situ by coupling the self-developed MIR hollow optical fiber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrared spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the changes in macromolecular content and structure in breast cancer tissues. For the first time, a trinary classification model was established based on 1D-CNN for recognizing normal, paracancerous and cancerous tissues. The final predication results reveal that the 1D-CNN model based on baseline correction (BC) and data augmentation yields more precise classification results, with a total accuracy of 95.09%, exhibiting superior discrimination ability than machine learning models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67.78%) and PCA-KNN (70.00%). The experimental results suggest that the application of 1D-CNN enables accurate classification and recognition of different breast tissues, which can be considered as a precise, efficient and intelligent novel method for breast cancer diagnosis.