An modified RamanNet model integrated with serum Raman spectroscopy for breast cancer screening

Abstract

Based on the characteristics of spectral data, Nabil Ibtehaz et al. (2023) proposed a generalized neural network architecture for Raman spectroscopy analysis, called RamanNet. This paper applies it to breast cancer screening and proposes an modified RamanNet method to optimize the classification performance of breast cancer and healthy individuals. The modified model accelerates convergence and reduces overfitting by incorporating L2 regularization, removing TripletLoss, and adjusting the learning rate. Results demonstrate that the modified RamanNet achieved a higher accuracy (96.0 ± 1.7 %) and sensitivity (96.8 ± 3.0 %) in distinguishing between breast cancer patients and healthy controls, outperforming both the 1D-CNN (accuracy: 91.8 ± 2.9 %; sensitivity: 89.3 ± 5.1 %) and the original RamanNet (accuracy: 92.5 ± 3.2 %; sensitivity: 94.6 ± 5.6 %). Furthermore, the model demonstrated enhancements in training time, convergence speed and stability, which provides a new technological approach for non-invasive and rapid breast cancer screening with great potential for clinical application.