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

Crossref DOI link: https://doi.org/10.1186/s12885-025-13926-2

Published Online: 2025-04-03

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Taghados, Zahra

Azimifar, Zohreh

Monsefi, Malihezaman

Jahromi, Mojgan Akbarzadeh
License Information

Text and Data Mining valid from 2025-04-03

Version of Record valid from 2025-04-03
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Article History

Received: 13 February 2024

Accepted: 12 March 2025

First Online: 3 April 2025

Declarations

:

: The study protocol was approved by the Ethics Committee of Shiraz University of Medical Sciences (SUMS) under the approval code IR.SUMS.REC.1403.096. The research was conducted in compliance with the Declaration of Helsinki to uphold ethical standards in human research. The Ethics Committee of Shiraz University of Medical Sciences determined that obtaining informed consent was not required, as the study did not involve any personally identifiable information or direct interaction with participants. If required, we can provide a PDF file of the approval document.

: This study does not include any identifiable patient data or images requiring consent for publication. Therefore, this section is not applicable.

: The authors declare no competing interests.

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