Accurate screening of early-stage lung cancer based on improved ResNeXt model combined with serum Raman spectroscopy

Screening and diagnosis of early-stage lung cancer is low worldwide, and when lung cancer progresses to late stage, it will greatly affect the treatment and survival time of patients. Therefore, the development of an affordable and accurate diagnostic technique is critical for lung cancer patients. In this study, we analyzed and verified the changes of substance composition in the serum of early-stage lung cancer patients and proposed an improved ResNeXt model to achieve accurate classification of serum Raman spectra of lung cancer. The robustness of the model is improved by adding different decibels of Gaussian white noise and spectral offset to augment the data, trying to process the augmented data with various pre-processing methods, and inputting the pre-processed data into the improved ResNeXt model, the improved ResNeXt model still shows the optimal performance after sufficient comparison experiments with three other more advanced deep learning models. The experimental results show that the improved ResNeXt model is very suitable for the classification of early-stage lung cancer serum Raman spectra and may provide a reference for future early screening studies of other cancers.