Fluorescence lifetime diagnosis of cervical cancer based on Extreme Learning Machine
Jun Gu, Beng Koon Ng, Fu Chit Yaw, Sirajudeen Gulam Razul, Lim Soo Kim · 2010
Fluorescence Lifetime Imaging (FLIM) was used to study the histopathological conditions of cervical biopsy tissues. Measurements were conducted on more than 40 H&E stained cervical tissue sections. The characteristic decay lifetimes of the samples were extracted using an Expectation-Maximization and Bayesian Information Criterion algorithm. Diagnostic criterion based on the Extreme Learning Machine was developed to discriminate between normal and neoplastic samples. A high sensitivity and specificity of more than 80%were obtained. The proposed technique can be used to automate and supplement the traditional histopathological examination of cervical tissues.