Introducing the Enhanced Cervical Cancer Detection Model PapEMS-Net, Integrating EfficientNet, Multi-Verse Optimizer, and Softmax Entropy Classifier
Received: 24 June 2025 | Revised: 19 August 2025 | Accepted: 30 August 2025 | Online: 8 December 2025
Corresponding author: N. Rama Rao
Abstract
Cervical cancer remains a leading cause of mortality in low-resource regions where early diagnosis is often delayed due to limited access to expert cytologists and manual screening methods. To address the need for a robust, automated diagnostic system, this study proposes PAP smear EfficientNet-MVO-Softmax classifier Network (PapEMS-Net), a deep learning-based architecture designed for accurate classification of cervical cell types in PAP smear images. This study introduces PapEMS-Net, a deep learning framework designed to classify cervical cells in PAP smear images. By combining EfficientNet, Multi-Verse Optimizer (MVO), and a Softmax Entropy Classifier, the model provides accurate and efficient classification. The proposed PapEMS-Net model, evaluated on the Single-cell PAP Smear Image Dataset for Medical Diagnosis (SIPaKMeD), achieved an accuracy of 99.43%, precision of 99.29%, recall of 99.37%, and F1-score of 99.31%, outperforming existing models. The visual analysis through confusion matrix, ROC curves, and predicted class distributions further validated the model’s discriminative capability across all five cytological classes.
Keywords:
cervical cancer, PAP smear, accuracy, optimization, deep learning, detectionDownloads
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Copyright (c) 2025 M. Sandhya Vani, N. Rama Rao
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