Empirical Mode Decomposition and Grassmann Manifold‐Based Cervical Cancer Detection

• Published in: Journal of biophotonics Vol. 18; no. 7; pp. e202400584 - n/a
• Main Authors: Nayak, Sidharthenee, Deo, Bhaswati Singha, Pal, Mayukha, Panigrahi, Prasanta K., Pradhan, Asima
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.07.2025; Wiley Subscription Services, Inc
• Subjects: Cancer; Cervical cancer; Cervix; Decomposition; empirical mode decomposition; Female; Females; Fluorescence; Fluorescence spectroscopy; Grassmann manifold; Humans; Machine Learning; Malignancy; Manifolds (mathematics); mutual information; Radio frequency; random Forest; Representations; Reproductive system; Spectrometry, Fluorescence; Uterine Cervical Neoplasms - diagnosis; Uterine Cervical Neoplasms - diagnostic imaging; fluorescence spectroscopy; Grassmann manifold; empirical mode decomposition; mutual information; cervical cancer; random Forest

ABSTRACT Cervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful tre...