Abstract
Early and accurate detection through Pap smear analysis is critical to improving patient outcomes and reducing mortality of Cervical cancer. State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) require substantial computational resources, extended training time, and large datasets. In this study, a lightweight CNN model, S-Net (Simple Net), is developed specifically for cervical cancer detection and classification using Pap smear images to address these limitations. Alongside S-Net, six SOTA CNNs were evaluated using transfer learning, including multi-path (DenseNet201, ResNet152), depth-based (Serasnet152), width-based multi-connection (Xception), depth-wise separable convolutions (MobileNetV2), and spatial exploitation-based (VGG19). All models, including S-Net, achieved comparable accuracy, with S-Net reaching 99.99%. However, S-Net significantly outperforms the SOTA CNNs in terms of computational efficiency and inference time, making it a more practical choice for real-time and resource-constrained applications. A major limitation in CNN-based medical diagnosis remains the lack of transparency in the decision-making process. To address this, Explainable AI (XAI) techniques, such as SHAP, LIME, and Grad-CAM, were employed to visualize and interpret the key image regions influencing model predictions. The novelty of this study lies in the development of a highly accurate yet computationally lightweight model (S-Net) caPable of rapid inference while maintaining interpretability through XAI integration. Furthermore, this work analyzes the behavior of SOTA CNNs, investigates the effects of negative transfer learning on Pap smear images, and examines pixel intensity patterns in correctly and incorrectly classified samples.
Abstract (translated)
通过巴氏涂片分析的早期和准确检测对于改善宫颈癌患者的预后并降低其死亡率至关重要。最先进的（SOTA）卷积神经网络（CNNs）需要大量的计算资源、长时间的训练以及大规模的数据集。在这项研究中，开发了一种轻量级的CNN模型——S-Net（简易网），专门用于使用巴氏涂片图像进行宫颈癌的检测和分类，以此来解决这些问题。 除了S-Net外，还利用迁移学习评估了六种最先进的CNNs，包括多路径（DenseNet201、ResNet152）、基于深度（Seranet152）、基于宽度的多重连接（Xception）、逐层可分离卷积（MobileNetV2）和基于空间探索（VGG19）。所有模型，包括S-Net，在准确性方面都达到了相当高的水平，其中S-Net的准确率达到99.99%。然而，与最先进的CNNs相比，S-Net在计算效率和推理时间上表现出色，使其成为实时应用及资源受限环境中的更优选择。 基于CNN的医学诊断的一个主要限制在于决策过程缺乏透明度。为解决这一问题，采用了可解释的人工智能（XAI）技术，如SHAP、LIME和Grad-CAM，以可视化并解释影响模型预测的关键图像区域。本研究的独特之处在于开发了一种高精度且计算效率高的模型——S-Net，能够在保持通过XAI集成的可解释性的同时实现快速推理。此外，这项工作还分析了最先进的CNNs的行为、探讨了负向迁移学习对巴氏涂片图像的影响，并检查了正确分类和错误分类样本中的像素强度模式。 这段翻译详细介绍了研究背景、模型开发目标以及所采用的技术手段和创新点，强调了S-Net在宫颈癌检测领域的应用价值及其优势。
URL
https://arxiv.org/abs/2509.16250
