面向宫颈癌检测的深度卷积神经网络、迁移学习与Mnet模型研究
A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
AI总结：
本文提出轻量级CNN模型S-Net用于宫颈癌巴氏涂片检测，在达到99.99%准确率的同时显著降低计算成本，并结合XAI技术提升模型可解释性。
摘要：
通过巴氏涂片分析实现早期且准确的检测，对于改善宫颈癌患者预后和降低死亡率至关重要。最先进的卷积神经网络（CNNs）需要大量计算资源、较长训练时间和大规模数据集。在本研究中，专门开发了一种轻量级CNN模型S-Net（Simple Net），用于利用巴氏涂片图像进行宫颈癌检测和分类，以解决上述局限性。除S-Net外，还使用迁移学习评估了六种最先进的CNN，包括多路径网络（DenseNet201、ResNet152）、基于深度的网络（Serasnet152）、基于宽度的多连接网络（Xception）、深度可分离卷积网络（MobileNetV2）以及基于空间利用的网络（VGG19）。包括S-Net在内的所有模型都取得了相当的准确率，S-Net达到99.99%。然而，S-Net在计算效率和推理时间方面显著优于最先进的CNN，使其成为实时和资源受限应用中更实用的选择。基于CNN的医学诊断的一个主要局限仍然是决策过程缺乏透明度。为解决这一问题，采用了可解释人工智能（XAI）技术，如SHAP、LIME和Grad-CAM，来可视化和解释影响模型预测的关键图像区域。本研究的新颖之处在于开发了一种高精度且计算轻量的模型（S-Net），该模型能够快速推理，同时通过XAI集成保持可解释性。此外，本工作分析了最先进CNN的行为，研究了负迁移学习对巴氏涂片图像的影响，并考察了正确分类和错误分类样本中的像素强度模式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
