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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BMC
2025-04-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-13926-2 |
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| author | Zahra Taghados Zohreh Azimifar Malihezaman Monsefi Mojgan Akbarzadeh Jahromi |
| author_facet | Zahra Taghados Zohreh Azimifar Malihezaman Monsefi Mojgan Akbarzadeh Jahromi |
| author_sort | Zahra Taghados |
| collection | DOAJ |
| description | 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, with the majority occurring in low- and middle-income countries. These figures emphasize the critical need for effective prevention, early detection, and diagnostic strategies. Recent advancements in machine learning (ML) and deep learning (DL) have greatly enhanced the accuracy of cervical cancer cell classification and diagnosis in manual screening. However, traditional predictive approaches often lack interpretability, which is critical for building explainable AI systems in medicine. Integrating causal reasoning, causal inference, and causal discovery into diagnostic frameworks addresses these challenges by uncovering latent causal relationships rather than relying solely on observational correlations. This ensures greater consistency, comprehensibility, and transparency in medical decision-making. This study introduces CausalCervixNet, a Convolutional Neural Network with Causal Insight (CICNN) tailored for cervical cancer cell classification. By leveraging causality-based methodologies, CausalCervixNet uncovers hidden causal factors in cervical cell images, enhancing both diagnostic accuracy and efficiency. The approach was validated on three datasets: SIPaKMeD, Herlev, and our self-collected ShUCSEIT (Shiraz University-Computer Science, Engineering, and Information Technology) dataset, containing detailed cervical cell cytopathology images. The proposed framework achieved classification accuracies of 99.14%, 97.31%, and 99.09% on the SIPaKMeD, Herlev, and ShUCSEIT datasets, respectively. These results highlight the importance of integrating causal discovery, causal reasoning, and causal inference into diagnostic workflows. By merging causal perspectives with advanced DL models, this research offers an interpretable, reliable, and efficient framework for cervical cancer diagnosis, contributing to improved patient outcomes and advancements in cervical cancer treatment. |
| format | Article |
| id | doaj-art-2e5bf9332b86458f8cbf7cf47f0a8dae |
| institution | DOAJ |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-2e5bf9332b86458f8cbf7cf47f0a8dae2025-08-20T03:04:51ZengBMCBMC Cancer1471-24072025-04-0125111910.1186/s12885-025-13926-2CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracyZahra Taghados0Zohreh Azimifar1Malihezaman Monsefi2Mojgan Akbarzadeh Jahromi3Department of Computer Science, Engineering and Information Technology, Shiraz UniversityDepartment of Computer Science, Engineering and Information Technology, Shiraz UniversityDepartment of Biology, Shiraz UniversityDepartment of Pathology, Shiraz University of Medical SciencesAbstract 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, with the majority occurring in low- and middle-income countries. These figures emphasize the critical need for effective prevention, early detection, and diagnostic strategies. Recent advancements in machine learning (ML) and deep learning (DL) have greatly enhanced the accuracy of cervical cancer cell classification and diagnosis in manual screening. However, traditional predictive approaches often lack interpretability, which is critical for building explainable AI systems in medicine. Integrating causal reasoning, causal inference, and causal discovery into diagnostic frameworks addresses these challenges by uncovering latent causal relationships rather than relying solely on observational correlations. This ensures greater consistency, comprehensibility, and transparency in medical decision-making. This study introduces CausalCervixNet, a Convolutional Neural Network with Causal Insight (CICNN) tailored for cervical cancer cell classification. By leveraging causality-based methodologies, CausalCervixNet uncovers hidden causal factors in cervical cell images, enhancing both diagnostic accuracy and efficiency. The approach was validated on three datasets: SIPaKMeD, Herlev, and our self-collected ShUCSEIT (Shiraz University-Computer Science, Engineering, and Information Technology) dataset, containing detailed cervical cell cytopathology images. The proposed framework achieved classification accuracies of 99.14%, 97.31%, and 99.09% on the SIPaKMeD, Herlev, and ShUCSEIT datasets, respectively. These results highlight the importance of integrating causal discovery, causal reasoning, and causal inference into diagnostic workflows. By merging causal perspectives with advanced DL models, this research offers an interpretable, reliable, and efficient framework for cervical cancer diagnosis, contributing to improved patient outcomes and advancements in cervical cancer treatment.https://doi.org/10.1186/s12885-025-13926-2Causal discoveryCausal inferenceCausal reasoningCausalityCervical cancerClassification |
| spellingShingle | Zahra Taghados Zohreh Azimifar Malihezaman Monsefi Mojgan Akbarzadeh Jahromi CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy BMC Cancer Causal discovery Causal inference Causal reasoning Causality Cervical cancer Classification |
| title | CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy |
| title_full | CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy |
| title_fullStr | CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy |
| title_full_unstemmed | CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy |
| title_short | CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification—leveraging deep learning models for enhanced diagnostic accuracy |
| title_sort | causalcervixnet convolutional neural networks with causal insight cicnn in cervical cancer cell classification leveraging deep learning models for enhanced diagnostic accuracy |
| topic | Causal discovery Causal inference Causal reasoning Causality Cervical cancer Classification |
| url | https://doi.org/10.1186/s12885-025-13926-2 |
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