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
• ISSN: 1864-063X; 1864-0648; 1864-0648
• DOI: 10.1002/jbio.202300363

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 specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in‐vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in‐house developed handheld probe (i‐HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10‐fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i‐HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics. The proposed method with a handheld system, overcomes the intrinsic spectral signature variations where an information theory‐based approach combined with AI/ML achieves 93.17% classification accuracy in diagnosing cervical cancer from a small, and diverse dataset.