Open AccessDevelopment of a portable Raman device with artificial intelligence method for the detection and staging of endometrial cancer
Author(s) -
Nambudiri Manu Krishnan Krishnan,
Rajanbabu Anupama,
Nair Indu Ramachandran,
Nair Shantikumar V.,
Koyakutty Manzoor,
Madathil Girish Chundayil
Publication year - 2023
Publication title -
translational biophotonics
Language(s) - English
Resource type - Journals
ISSN - 2627-1850
DOI - 10.1002/tbio.202200014
Subject(s) - raman spectroscopy , artificial intelligence , computer science , principal component analysis , pattern recognition (psychology) , classifier (uml) , convolutional neural network , support vector machine , machine learning , physics , optics
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.