Influence of preprocessing of stimulated Raman scattering images on the performance of deep neural networks for detecting cancer tissue.

BACKGROUND

Images obtained from stimulated Raman scattering can be used to identify histomorphologically relevant information intraoperatively. In order to leverage deep learning algorithms for distinguishing tumoral and non-tumoral tissue, data preprocessing remains a crucial task and may affect the classification performance. To date, the effect of different preprocessing techniques on deep learning algorithm performance is unclear. This study aims to make a contribution to closing this knowledge gap.

METHODS

To investigate the influence of different preprocessing techniques of images obtained from stimulated Raman scattering, six deep learning architectures (VGG19, ResNet50, InceptionResNetV2, Xception, ConvNeXt and Vision Transformer) and five different preprocessing procedures were compared. For this, annotated datasets comprising 542 images of tissue samples obtained from patients with oral squamous cell carcinoma and non-small cell lung carcinoma were used for network training. Each network was trained five times for 40 epochs. Performance metrics balanced accuracy, precision, recall and F1-score were recorded. Class activation and attention maps were used to highlight on which input pixels a prediction is based.

RESULTS

A scaling of the original pixel values of stimulated Raman scattering images to the range [0, 1] yielded a higher and more stable overall classification performance across the neural networks when compared to more sophisticated and computationally expensive methods [ =0.8327; standard deviation (SD) =0.0622 on scaled dataset and =0.7213 (SD =0.2315) on complex preprocessed dataset; P≤0.05]. Absolute performance was best on stimulated Raman histology images ( =0.8478; SD =0.1487).

CONCLUSIONS

This study shows that preprocessing of pixel values of stimulated Raman scattering images can have a great impact on the performance and the stability of deep learning algorithms when applied for classification of cancer tissue.