Fekri-Ershad Shervan, Alsaffar Marwa Fadhil
Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran.
Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran.
Diagnostics (Basel). 2023 Feb 12;13(4):686. doi: 10.3390/diagnostics13040686.
Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.
宫颈癌是女性中最常见的癌症类型之一，其死亡率高于许多其他癌症类型。诊断宫颈癌最常见的方法是分析宫颈细胞图像，这是通过巴氏涂片成像测试来进行的。早期准确的诊断可以挽救许多患者的生命，并增加治疗方法成功的几率。到目前为止，已经提出了各种基于巴氏涂片图像分析来诊断宫颈癌的方法。现有的大多数方法可以分为基于深度学习技术或机器学习算法的两组方法。在本研究中，提出了一种组合方法，其总体结构基于机器学习策略，其中特征提取阶段与分类阶段完全分开。然而，在特征提取阶段，使用了深度网络。本文提出了一种由深度特征馈送的多层感知器（MLP）神经网络。基于四个创新理念调整隐藏层神经元的数量。此外，还使用了ResNet-34、ResNet-50和VGG-19深度网络来馈送MLP。在所提出的方法中，在这两个卷积神经网络中去除与分类阶段相关的层，其输出在经过一个展平层后馈入MLP。为了提高性能，这两个卷积神经网络都使用Adam优化器在相关图像上进行训练。所提出的方法在Herlev基准数据库上进行了评估，在两类情况下提供了99.23%的准确率，在七类情况下提供了97.65%的准确率。结果表明，所提出的方法比基线网络和许多现有方法具有更高的准确率。
