UROGENITAL RADIOLOGY / ORIGINAL PAPER
Attention-enhanced deep learning for cervical cytology: combining convolutional networks with multi-head attention and fuzzy logic
1
School of Computing, DIT University, Dehradun, India
2
Computing Science and Engineering, Chinmaya Vishwa Vidyapeeth, Ernakulam, India
Submission date: 2025-04-02
Final revision date: 2025-06-04
Acceptance date: 2025-06-23
Publication date: 2025-08-20
Pol J Radiol, 2025; 90: 414-430
KEYWORDS
TOPICS
ABSTRACT
Purpose:
Cervical cancer continues to be one of the leading causes of death among females worldwide, and thus early diagnosis by using more advanced diagnostic procedures is crucial. The conventional Pap-smear procedure is accurate but subject to human error; thus, computerised, standardised, and automated diagnosis becomes imperative. Herein we present a novel framework of a fuzzy distance-based ensemble of convolutional neural networks (CNNs) for efficient cervical cancer classification from Pap-smear images.
Material and methods:
The proposed approach integrates 5 models of CNN – Simple CNN, InceptionV3, Xception, Xception with Attention, and Inception Attention – via attention mechanisms to advance feature learning. A fuzzy distance-based aggregator function is introduced to fuse the predictions of these models optimally as per Euclidean, Manhattan, and cosine distance measures. Four advanced pre-processing techniques – wavelet denoising, contrast-limited adaptive histogram equalisation (CLAHE), background correction, and Laplacian sharpening – are employed to construct a cleaner dataset with enhanced image sharpness and segmentation.
Results:
Experimental outcomes prove that the model is significantly better than state-of-the-art approaches, with an accuracy of 94% on the original dataset and 98.3% on the pre-processed dataset.
Conclusions:
The method suggested herein has better noise robustness, interpretability through fuzzy logic, and automatic adaptation to various CNN frameworks without fine-tuning. These results acknowledge the promise of fuzzy logic-based CNN ensembles to improve machine-based cervical cancer diagnosis, which could be mapped to better and scalable diagnostic instruments in medical imaging.
Cervical cancer continues to be one of the leading causes of death among females worldwide, and thus early diagnosis by using more advanced diagnostic procedures is crucial. The conventional Pap-smear procedure is accurate but subject to human error; thus, computerised, standardised, and automated diagnosis becomes imperative. Herein we present a novel framework of a fuzzy distance-based ensemble of convolutional neural networks (CNNs) for efficient cervical cancer classification from Pap-smear images.
Material and methods:
The proposed approach integrates 5 models of CNN – Simple CNN, InceptionV3, Xception, Xception with Attention, and Inception Attention – via attention mechanisms to advance feature learning. A fuzzy distance-based aggregator function is introduced to fuse the predictions of these models optimally as per Euclidean, Manhattan, and cosine distance measures. Four advanced pre-processing techniques – wavelet denoising, contrast-limited adaptive histogram equalisation (CLAHE), background correction, and Laplacian sharpening – are employed to construct a cleaner dataset with enhanced image sharpness and segmentation.
Results:
Experimental outcomes prove that the model is significantly better than state-of-the-art approaches, with an accuracy of 94% on the original dataset and 98.3% on the pre-processed dataset.
Conclusions:
The method suggested herein has better noise robustness, interpretability through fuzzy logic, and automatic adaptation to various CNN frameworks without fine-tuning. These results acknowledge the promise of fuzzy logic-based CNN ensembles to improve machine-based cervical cancer diagnosis, which could be mapped to better and scalable diagnostic instruments in medical imaging.
REFERENCES (44)
1.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68: 394-424.
2.
Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J, Bray F. Estimates of incidence and mortality of cervical cancer in 2018: a worldwide analysis. Lancet Global Health 2020; 8: e191-e203.
3.
Kessler TA. Cervical cancer: prevention and early detection. Semin Oncol Nurs 2017; 33: 172-183.
4.
Lozano R. Comparison of computer-assisted and manual screening of cervical cytology. Gynecol Oncol 2007; 104: 134-138.
5.
Ali MM, Ahmed K, Bui FM, Paul BK, Ibrahim SM, Quinn JM, Moni MA. Machine learning-based statistical analysis for early stage detection of cervical cancer. Comput Biol Med 2021; 139: 104985.
6.
Kaushik M, Joshi RC, Kushwah AS, Gupta MK, Banerjee M, Burget R, Dutta MK. Cytokine gene variants and socio-demographic characteristics as predictors of cervical cancer: a machine learning approach. Comput Biol Med 2021; 134: 104559.
7.
Rahaman MM, Li C, Yao Y, Kulwa F, Wu X, Li X, Wang Q. DeepCervix: a deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques. Comput Biol Med 2021; 136: 104649.
8.
Arel I, Rose DC, Karnowski TP. Deep machine learning – a new frontier in artificial intelligence research [research frontier]. IEEE Comput Intell Mag 2010; 5: 13-18.
10.
Paul A, Pramanik R, Malakar S, Sarkar R. An ensemble of deep transfer learning models for handwritten music symbol recognition. Neural Comput Applic 2022; 34: 10409–10427.
11.
Banerjee A, Singh PK, Sarkar R. Fuzzy integral-based CNN classifier fusion for 3D skeleton action recognition. IEEE Trans Circuits Syst Video Technol 2021; 31: 2206-2216.
12.
Chakraborty N, Kundu S, Paul S, Mollah AF, Basu S, Sarkar R. Language identification from multi-lingual scene text images: a CNN based classifier ensemble approach. J Ambient Intell Humanized Comput 2021; 12: 7997-8008.
13.
Tulyakov S, Jaeger S, Govindaraju V, Doermann D. Review of classifier combination methods. Mach Learn Doc Anal Recognit 2008, p. 361-386.
14.
Kaur H, Sharma R, Kaur J. Comparison of deep transfer learning models for classification of cervical cancer from Pap smear images. Sci Rep 2025; 15: 3945. DOI: https://doi.org/10.1038/s41598....
15.
Bechar A, Medjoudj R, Elmir Y, Himeur Y, Amira A. Federated and transfer learning for cancer detection based on image analysis. Neural Comput Applic 2025; 37: 2239-2284.
16.
Mehedi MHK, Khandaker M, Ara S, Alam MA, Mridha MF, Aung Z. A lightweight deep learning method to identify different types of cervical cancer. Sci Rep 2024; 14: 29446. DOI: https://doi.org/10.1038/s41598....
17.
Sambyal D, Sarwar A. Recent developments in cervical cancer diagnosis using deep learning on whole slide images: an overview of models, techniques, challenges and future directions. Micron 2023; 173: 103520. DOI: https://doi.org/10.1016/j.micr....
18.
Wubineh BZ, Rusiecki A, Halawa K. Segmentation and classification techniques for Pap smear images in detecting cervical cancer: a systematic review. IEEE Access 2024; 12: 118195-118213.
19.
Huang Q, Zhang W, Chen Y, Chen J, Yang Z. Review of cervical cell segmentation. Multimedia Tools and Applications 2024. DOI: https://doi.org/10.1007/s11042....
20.
Sarhangi HA, Beigifard D, Farmani E, Bolhasani H. Deep learning techniques for cervical cancer diagnosis based on pathology and colposcopy images. Informatics in Medicine Unlocked 2024; 47: 101503. DOI: https://doi.org/10.1016/j.imu.....
21.
Hemalatha K, Vetriselvi V, Dhandapani M. CervixFuzzyFusion for cervical cancer cell image classification. Biomedical Signal Processing and Control 2023; 85: 104920. DOI: https://doi.org/10.1016/j.bspc....
22.
de Lima CR, Khan SG, Shah SH, Ferri L. Mask region-based CNNs for cervical cancer progression diagnosis on Pap smear examinations. Heliyon 2023; 9: e21388. DOI: https://doi.org/10.1016/j.heli....
23.
Ali MS, Hossain MM, Kona MA, Nowrin KR, Islam MK. An ensemble classification approach for cervical cancer prediction using behavioral risk factors. Healthcare Analytics 2024; 5: 100324. DOI: https://doi.org/10.1016/j.heal....
24.
Tan SL, Selvachandran G, Ding W, Paramesran R, Kotecha K. Cervical cancer classification from Pap smear images using deep convolutional neural network models. Interdisciplinary Sciences: Computational Life Sciences 2024; 16: 16-38.
25.
Nasir MU, Khalil OK, Ateeq K, Almogadwy A, Saleem B, Khan MA, et al. Cervical cancer prediction empowered with federated machine learning. Computers, Materials & Continua 2024; 79. DOI: https://doi.org/10.32604/cmc.2....
26.
Joynab NS, Islam MN, Aliya RR, Hasan AR, Khan NI, Sarker IH. A federated learning aided system for classifying cervical cancer using Pap-smear images. Informatics in Medicine Unlocked 2024; 47: 101496. DOI: https://doi.org/10.1016/j.imu.....
27.
Sharma V, Kumar A, Sharma K. Digital twin application in women’s health: Cervical cancer diagnosis with CervixNet. Cogn Syst Res 2024; 87: 101264. DOI: https://doi.org/10.1016/j.cogs....
28.
Xie H, Tan T, Zhang H, Li Q. Dose prediction for cervical cancer in radiotherapy based on the beam channel generative adversarial network. Heliyon 2024; 10. DOI: https://doi.org/10.1016/j.heli....
29.
Mathivanan SK, Francis D, Srinivasan S, Khatavkar V, Karthikeyan P, et al. Enhancing cervical cancer detection and robust classification through a fusion of deep learning models. Sci Rep 2024; 14: 10812. DOI: https://doi.org/10.1038/s41598....
30.
Khowaja A, Zou B, Kui X. Enhancing cervical cancer diagnosis: Integrated attention-transformer system with weakly supervised learning. Image and Vision Computing 2024; 149: 105193. DOI: https://doi.org/10.1016/j.imav....
31.
Kawahara D, Yoshimura H, Murakami Y, Matsuura T, Nagata Y. Usability of synthesized image using generative adversarial network for prediction model of recurrence after radiotherapy in locally advanced cervical cancer. Biomedical Signal Processing and Control 2024; 89: 105762. DOI: https://doi.org/10.1016/j.bspc....
32.
Madathil S, Dhouib M, Lelong Q, Bourassine A, Monsonego J. A multimodal deep learning model for cervical pre-cancers and cancers prediction: development and internal validation study. Comput Biol Med 2025; 186: 109710. DOI: https://doi.org/10.1016/j.comp....
33.
Sharma AK, Nandal A, Dhaka A, Alhudhaif A, Polat K, Sharma A. Diagnosis of cervical cancer using CNN deep learning model with transfer learning approaches. Biomedical Signal Processing and Control 2025; 105: 107639. DOI: https://doi.org/10.1016/j.bspc....
34.
Hemalatha K, Vetriselvi V. Self-supervised learning using diverse cell images for cervical cancer classification. Measurement 2025; 243: 116413. DOI: https://doi.org/10.1016/j.meas....
35.
Wang J, Yu Y, Tan Y, Wan H, Zheng N, He Z, et al. Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer. Nat Commun 2024; 15: 4369. DOI: https://doi.org/10.1038/s41467....
36.
Pacal I. MaxCerVixT: A novel lightweight vision transformer-based approach for precise cervical cancer detection. Knowledge-Based Systems 2024; 289: 111482. DOI: https://doi.org/10.1016/j.knos.... 2024.111482.
37.
Sharma A, Parvathi R. Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3. IEEE Access 2025. DOI: https://doi.org/10.1109/ACCESS....
38.
Munshi RM. Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction. PLoS One 2024; 19: e0296107. DOI: https://doi.org/10.1371/journa....
39.
Aljrees T. Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning. PLoS One 2024; 19: e0295632. DOI: https://doi.org/10.1371/journa....
40.
Muksimova S, Umirzakova S, Kang S, Cho YI. CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learningenhanced convolutional networks. Heliyon 2024; 10. DOI: https://doi.org/10.1016/j.heli....
41.
Taghados Z, Azimifar Z, Monsefi M, Jahromi MA. CausalCervixNet: convolutional neural networks with causal insight (CICNN) in cervical cancer cell classification – leveraging deep learning models for enhanced diagnostic accuracy. BMC Cancer 2025; 25: 607. DOI: https://link.springer.com/arti....
42.
Cömert Z, Efil F, Türkoğlu M. Convolutional Block Attention Module and Parallel Branch Architectures for Cervical Cell Classification. Int J Imaging Syst Technol 2025; 35: e70048. DOI: https://onlinelibrary.wiley.co....
43.
Zangana HM, Mustafa FM. Review of Hybrid Denoising Approaches in Face Recognition: Bridging Wavelet Transform and Deep Learning. Indonesian Journal of Computer Science 2024; 13. DOI: http://ijcs.net/ijcs/index.php....
44.
Kaur N. Hybrid image splicing detection: Integrating CLAHE, improved CNN, and SVM for digital image forensics. Expert Systems with Applications 2025. DOI: https://www.sciencedirect.com/....
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
