Study Of Cervical Precancerous Lesions Detection By Spectroscopy And Support Vector Machine

Background and objective: Diffuse reflectance spectroscopy (DRS) offers a fast, non-invasive, and low-cost alternative for cervical cancer diagnosis. We aim to develop a method for screening precancerous lesions based on DRS. Material and methods: Characteristic parameters of cervical tissue were extracted from spectra, including optical characteristic parameters such as absorption and scattering coefficients, and some slope and area parameters of the spectrum. Data were randomly divided into training (60%) and test (40%) sets. Of the 210 included patients, 166 were healthy, 22 had erosion of the cervix, and 31 had cervical intraepithelial neoplasia (CIN). The support vector machine (SVM) algorithm was used to classify normal and abnormal cervical tissue based on 11 characteristic parameters. Results: The SVM with linear kernel function, applied on the training data, could distinguish tissue with lesions from healthy tissue with an accuracy of 1.00. When the classifiers were applied to the test set, erosion of cervix and CIN could be discriminated from healthy tissue with an accuracy of 0.95 (0.03). Conclusions: This research shows that the diagnostic algorithm can be valuable for non-invasive diagnosis of cervical cancer. This is a significant step toward the development of a tool for tissue assessment of cervical cancer.