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<p>Cervical cancer remains a leading cause of cancer-related mortality among women worldwide, yet its progression is largely preventable through timely and accurate diagnosis. Conventional Pap smear screening pipelines, however, are constrained by subjective interpretation, diagnostic complexity, and limited throughput — barriers that impede scalable deployment in resource-limited settings. To address these critical gaps, this study presents EQS-NET, a novel, lightweight, and feature-optimized Computer-Aided Diagnosis (CAD) framework that leverages a heterogeneous ensemble of compact CNNs — ShuffleNet, SqueezeNet, and EfficientNet — to extract complementary deep representations via transfer learning. EQS-NET employs a Multi-Layer Deep Feature Fusion strategy aggregating discriminative features across the final three convolutional layers of each network, refined through mRMR-based feature selection. The framework eliminates the need for hand-crafted feature engineering, image segmentation, and cytology-specific preprocessing, relying only on minimal standard preprocessing. Validated under stratified 5-fold cross-validation on two benchmark datasets, EQS-NET achieves 98.5% ± 0.18% on SIPaKMeD and 99.98% ± 0.04% on Mendeley LBC (p &lt; 0.001 against single-CNN baselines), outperforming existing state-of-the-art methods across sensitivity, specificity, and AUC — establishing it as a scalable, efficient, and clinically viable solution for automated cervical cancer screening.</p>

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EQS-net for feature-optimized cervical cancer diagnosis using multi-layer fusion & dimensionality reduction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Bhawna Swarnkar,
  • Nilay Khare,
  • Manasi Gyanchandani,
  • Lalit Kumar,
  • Nitin Kumar

摘要

Cervical cancer remains a leading cause of cancer-related mortality among women worldwide, yet its progression is largely preventable through timely and accurate diagnosis. Conventional Pap smear screening pipelines, however, are constrained by subjective interpretation, diagnostic complexity, and limited throughput — barriers that impede scalable deployment in resource-limited settings. To address these critical gaps, this study presents EQS-NET, a novel, lightweight, and feature-optimized Computer-Aided Diagnosis (CAD) framework that leverages a heterogeneous ensemble of compact CNNs — ShuffleNet, SqueezeNet, and EfficientNet — to extract complementary deep representations via transfer learning. EQS-NET employs a Multi-Layer Deep Feature Fusion strategy aggregating discriminative features across the final three convolutional layers of each network, refined through mRMR-based feature selection. The framework eliminates the need for hand-crafted feature engineering, image segmentation, and cytology-specific preprocessing, relying only on minimal standard preprocessing. Validated under stratified 5-fold cross-validation on two benchmark datasets, EQS-NET achieves 98.5% ± 0.18% on SIPaKMeD and 99.98% ± 0.04% on Mendeley LBC (p < 0.001 against single-CNN baselines), outperforming existing state-of-the-art methods across sensitivity, specificity, and AUC — establishing it as a scalable, efficient, and clinically viable solution for automated cervical cancer screening.