Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation

Sep 21, 2026

Cervical cancer is a major global health challenge, making early detection essential to improve prognosis and guide timely interventions. A customized convolutional neural network is presented for cervical cancerous cell detection, enhanced with skip connections to improve gradient flow and preserve spatial information. The SIPaKMeD dataset (4049 images, five categories) is used, and class imbalance is addressed through GAN-based data augmentation. The model classifies koilocytes, superficial-intermediate, parabasal, dyskeratotic, and metaplastic cells with 99.11% validation accuracy and 99.82% training accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Cervical cancer detection overview

Proposed customized CNN with skip mechanisms

Dataset and GAN-based augmentation

Classification results and performance

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation Gunjan Shandilya1, Sheifali Gupta1, Ahmad Almogren2, Salil Bharany1, Ayman Altameem3, Ateeq Ur Rehman4􀀍 & Seada Hussen5􀀍 Cervical cancer is one of the biggest challenges in global health, thus it forms a critical need for early detection technologies that could improve patient prognosis and inform treatment decisions. This development in the form of an early detection mechanism increases the chances of successful treatment and survival, as early diagnosis promptly offers interventions that can dramatically reduce the rate of deaths attributed to this disease. Here, a customized Convolutional Neural Network (CNN) model is proposed for cervical cancerous cell detection. It includes three convolutional layers with increasing filter sizes and max-pooling layers, followed by dropout and dense layers for improved feature extraction and robust learning. By using ResNet models as inspiration, the model further innovates by incorporating skip connections into the CNN design. By enabling direct feature transmission from earlier to later layers, skip links enhance gradient flow and help preserve important spatial information. By boosting feature propagation, this integration increases the model’s ability to recognize minute patterns in cervical cell images, hence increasing classification accuracy. In our methodology, the SIPaKMeD dataset has been employed which contains 4049 cervical cell images that are arranged into five different categories. To address class imbalance, Generative Adversarial Networks (GANs) have been applied for data augmentation; that is, synthetic images have been created, that improve the diversity of the dataset and further enhance the robustness of the same. The present model is astonishingly accurate in classifying five cervical cell types: koilocytes, superficialintermediate, parabasal, dyskeratotic, and metaplastic, thus significantly enhancing early detection and diagnosis of cervical cancer. The model gives an excellent performance because it has a validation accuracy of 99.11% and a training accuracy of 99.82% . It is a reliable model in the diagnosis of cervical cancerous cells because it ensures advancement in the computer-assisted cervical cancer detection system. Keywords Convolutional neural network (CNN), Generative adversarial networks (GANs), Skip connection, SipakMed dataset, Cervical cancer (CC), Deep learning (DL) Cervical cancer remains a major worldwide health issue, particularly in low- and middle-income countries where access to early detection and treatment options may be limited. Early and accurate diagnosis of cervical cancer is crucial for reducing mortality rates and improving patient outcomes. Traditional methods, such as 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India. 2Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633 Riyadh, Saudi Arabia. 3Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, 11543 Riyadh, Saudi Arabia. 4School of Computing, Gachon University, Seongnam-si 13120, Republic of Korea. 5Department of Electrical Power, Adama Science and Technology University, 1888 Adama, Ethiopia. 􀀍 email: [202411144@gachon.ac.kr](202411144@gachon.ac.kr); [seada.hussen@aastu.edu.et](seada.hussen@aastu.edu.et) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) Pap smears, have been widely used for screening, but they often rely on manual analysis, which can be tedious and susceptible to human error. New developments in artificial intelligence (AI) and deep learning (DL) offer promising solutions for the classification automation of cervic

FAQs

What problem does the study target in cervical cancer care?

The study addresses the need for early detection technologies that can improve prognosis and support treatment decisions by enabling accurate cervical cell classification.

How is the proposed model different from a basic CNN?

It uses a customized CNN architecture with ResNet-inspired skip connections to enhance gradient flow and preserve important spatial information for recognizing subtle image patterns.

How does the research handle class imbalance in the SIPaKMeD dataset?

Generative adversarial networks (GANs) are used to create synthetic images for underrepresented categories, increasing dataset diversity and improving robustness.

Sep 21, 2026

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