CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy

CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy

Abstract Cervical cancer is a significant global health issue affecting women worldwide, necessitating prompt detection and effective management. According to the World Health Organization (WHO), approximately 660,000 new cases of cervical cancer and 350,000 deaths were reported globally in 2022, wi...

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Bibliographic Details
Main Authors: Zahra Taghados, Zohreh Azimifar, Malihezaman Monsefi, Mojgan Akbarzadeh Jahromi
Format: Article
Language:English
Published: BMC 2025-04-01
Series:BMC Cancer
Subjects:
Causal discovery
Causal inference
Causal reasoning
Causality
Cervical cancer
Classification
Online Access:https://doi.org/10.1186/s12885-025-13926-2
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https://doi.org/10.1186/s12885-025-13926-2

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