Development of a portable Raman device with artificial intelligence method for the detection and staging of endometrial cancer

Abstract

Abstract The success of a Raman spectroscopy device in cancer detection lies in its ability to acquire high‐quality Raman signals from samples and to employ efficient classification algorithms in analysing spectral data. Portable Raman systems enabled with artificial intelligence tools are well adaptable to clinical settings and for accuracy for community‐level rapid screening. Here, we developed a robotic Raman device with a high‐efficiency Raman probe, validating it against endometrial cancers detecting high‐grade, low‐grade cancers and normal classes. Algorithms like principal component analysis‐discriminant analysis, and support vector machine were compared against the deep learning methodology; convolutional neural network (CNN) with and without data augmentation. Eventually, the system could classify high‐grade, low‐grade and normal tissues with an F1‐score of 91%, 94% and 97%, respectively. CNN with data augmentation proved to be the most dependable classifier that works well even in the presence of high background noise. Thus, we demonstrate a unique portable Raman device with AI tools for high‐sensitivity Raman analysis of endometrial cancer.

Keywords

• artificial intelligence
• cancer diagnosis
• convolutional neural network
• data augmentation
• endometrial cancer
• machine learning