Cervical pre‐cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe

• Published in: Journal of biophotonics Vol. 17; no. 3; pp. e202300363 - n/a
• Main Authors: Deo, Bhaswati Singha, Sah, Amar Nath, Shukla, Shivam, Pandey, Kiran, Singh, Sweta, Pal, Mayukha, Panigrahi, Prasanta K., Pradhan, Asima
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.03.2024; Wiley Subscription Services, Inc
• Subjects: Artificial intelligence; Cancer; Cervical cancer; Classification; convolutional neural network; entropy‐based features; Fluorescence; Fluorescent indicators; Information theory; intrinsic fluorescence; in‐vivo; Latent period; Machine learning; Optics; Real time; Sensitivity; Spectra; Spectral signatures; intrinsic fluorescence; entropy‐based features; in‐vivo; cervical cancer; convolutional neural network

Cervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and spec...