Automatic cervical cell segmentation and classification in Pap smears

Cervical cancer is one of the leading causes of cancer death in females worldwide. The disease can be cured if the patient is diagnosed in the pre-cancerous lesion stage or earlier. A common physical examination technique widely used in the screening is Papanicolaou test or Pap test. In this research, a method for automatic cervical cancer cell segmentation and classification is proposed. A single-cell image is segmented into nucleus, cytoplasm, and background, using the fuzzy C-means (FCM) clustering technique. Four cell classes in the ERUDIT and LCH datasets, i.e., normal, low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL), and squamous cell carcinoma (SCC), are considered. The 2-class problem can be achieved by grouping the last 3 classes as one abnormal class. Whereas, the Herlev dataset consists of 7 cell classes, i.e., superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. These 7 classes can also be grouped to form a 2-class problem. These 3 datasets were tested on 5 classifiers including Bayesian classifier, linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural networks (ANN), and support vector machine (SVM). For the ERUDIT dataset, ANN with 5 nucleus-based features yielded the accuracies of 96.20% and 97.83% on the 4-class and 2-class problems, respectively. For the Herlev dataset, ANN with 9 cell-based features yielded the accuracies of 93.78% and 99.27% for the 7-class and 2-class problems, respectively. For the LCH dataset, ANN with 9 cell-based features yielded the accuracies of 95.00% and 97.00% for the 4-class and 2-class problems, respectively. The segmentation and classification performances of the proposed method were compared with that of the hard C-means clustering and watershed technique. The results show that the proposed automatic approach yields very good performance and is better than its counterparts.     

糖尿病性视网膜图像的深度神经网络分类方法

针对传统的视网膜图像处理步骤复杂、泛化性差、缺少完整的自动识别系统等问题,提出了一套完整的基于深度神经网络的视网膜图像自动识别系统。首先,对图像进行去噪、归一化、数据扩增等预处理;然后,设计了紧凑的神经网络模型——CompactNet,CompactNet继承了AlexNet的浅层结构参数,深层网络参数则根据训练数据进行自适应调整;最后,针对不同的训练方法和不同的网络结构进行了性能测试。实验结果表明,CompactNet网络的微调方法要优于传统的网络训练方法,其分类指标可以达到0.87,与传统直接训练相比高出0.27;对于LeNet,AlexNet和CompactNet三种网络模型,CompactNet网络模型的分类准确率最高;并且通过实验证实了数据扩增等预处理方法的必要性。     

Probabilistic neural network for breast cancer classification

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.     

可变神经网络结构下的遥感影像光谱分解方法

多层感知神经网络(MLP)是主流的非线性分解方法,但是目前缺乏有效方法处理MLP分解结果中的丰度负值问题。为此,提出一种可变神经网络结构的方法,逐步去除负值丰度对应的端元,并调整相应的网络结构使之针对剩余的端元进行分解。通过武汉地区模拟TM遥感影像实验可以发现,该方法与传统MLP方法以及线性光谱分解方法的平均误差分别为0.077 7、0.081 9、0.094 3,说明该方法的分解精度高于其他2种分解方法,能克服丰度负值问题。     

基于卷积神经网络的图像生成方式分类方法

提出一种采用卷积神经网络对自然图像和文档扫描图像进行分类的方法,通过卷积和池化操作提取两类图像具有高区分度的特征,融合后得到分类判决结果。实验结果表明,所提出的分类方法在 SKL 图像库上分类精度超过93%。图像预处理对模型的精度以及模型训练收敛所需时间具有积极效果,经过图像预处理后训练的卷积神经网络模型对图像文字大小和图像格式顽健。     

基于SVM-MLP的乳腺癌预测

乳腺癌一直是影响女性健康最重要的问题之一,已经成为全球女性发病率最高的恶性肿瘤.近年来,利用机器学习和深度学习方法来诊断癌症已经成为发展较快的一个分支.通过使用逻辑回归模型(LR)、高斯核函数支持向量机(SVM)、前馈神经网络(MLP)对同一数据集进行预测,得出其中SVM迭代时间最短,前馈神经网络预测准确率最高.为了减...     

基于改进CNN的宫颈细胞自动分类算法

本文采用深度学习算法中的卷积神经网络对细胞图像进行识别, 实现对宫颈细胞图像的自动分类. 首先对宫颈细胞进行预处理, 通过细胞核裁剪解决图像输入尺寸不一的问题, 对图像进行翻转平移, 对数据集进行扩充, 并解决样本量不均衡的问题; 接着选取VGG-16网络进行改进, 使用改进后的VGG-16网络进行特征提取, 以及细胞分类; 并采用迁移学习的方法加载预训练网络参数, 进而加快参数收敛速度, 提高分类准确率; 最终通过对网络的训练, 得到了较好的分类结果, 将分类结果与人工提取特征设计分类器的方法相比, 分类的准确率有所提高, 二分类的准确率达97.3%, 七分类的准确率达89%. 实验结果表明: 卷积神经网络对宫颈细胞图像进行自动分类, 分类准确率相比较人工提取特征分类器效果较好, 且分类结果不受分割图像准确率的影响.     

基于CNN的湍流图像退化强度分类研究

湍流图像的复原一直是退化图像领域的研究热点,但依据湍流干扰强度对图像进行分类研究相对较少.不同场景的高空航拍图像进行大气湍流处理.调整湍流退化强度值,生成2000张对应的湍流干扰图像,再对这些图像进行预处理后送入卷积神经网络中进行湍流退化强度分类,最后通过优化搭建的卷积神经网络模型的激活函数以及对学习率的调整进一步提升分类准确率.实验表明,卷积神经网络对不同干扰强度的湍流退化图像分类准确率达到80%左右,结果表明该方法对大气湍流退化图像的复原具有一定指导意义.     

Fourier and wavelet descriptors for shape recognition using neural networks—a comparative study

This paper presents the application of three different types of neural networks to the 2-D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier and wavelet transformations of the data, describing the shape of the pattern. Application of different neural network structures associated with different preprocessing of the data results in different accuracy of recognition and classification. The numerical experiments performed for the recognition of simulated shapes of the airplanes have shown the superiority of the wavelet preprocessing associated with the self-organizing neural network structure. The integration of the individual classifiers based on the weighted summation of the signals from the neural networks has been proposed and checked in numerical experiments.     

多层前向人工神经网络图像分类算法

传统人工神经网络的输入均为向量形式,而图像由矩阵形式表示,因此,在用人工神经网络进行图像处理时,图像将以向量形式输入至神经网络,这破坏了图像的结构信息,从而影响了图像处理的效果。为了提高网络对图像的处理能力,文中借鉴了深度学习的思想与方法,引进了具有矩阵输入的多层前向神经网络。同时,采用传统的反向传播训练算法(BP)训练该网络,给出了训练过程与训练算法,并在USPS手写数字数据集上进行了数值实验。实验结果表明,相对于单隐层矩阵输入前向神经网络(2D-BP),所提多层网络具有较好的分类效果。此外,对于彩色图片分类问题,利用所提出的2D-BP网络,给出了一个有效的可行方法。     

Artificial neural networks to identify naturally existing disease severity status

Classification is a central endeavour in Biology. Heterogeneity of biological systems makes classification more challenging, but this is crucial for effective disease control and management. This study is a computational modelling attempt to classify a plant disease using visual symptoms to ease crop management programmes. Weligama coconut leaf wilt disease (WCLWD), a phytoplasma-borne coconut disease characterised by three foliar symptoms (flaccidity (bending of leaflets), yellowing and marginal necrosis) found in Sri Lanka, was used to demonstrate its applicability. Self-organising map (SOM) was optimised to discover naturally existing categories of WCLWD using foliar symptoms. Ward clustering of SOM identified three distinct disease categories. Results agreed with the nature of disease progression and are supported by K-means clustering. Conversion of SOM clusters to a parsimonious multi-layer perceptron (MLP) supported by a novel efficient network pruning algorithm regenerated identical results proving that precise models can be developed for WCLWD classification using these approaches. The MLP (100 %) outperformed counter propagation (CP) neural network (91 %) in generalisation ability indicating the validity of the MLP model. The study identified flaccidity as the most influential symptom followed by yellowing and necrosis. Comparison of our results with expert decision on disease severity classification revealed 73.45 % correspondence. In-depth investigation into the results from the two approaches using statistical methods revealed that when multiple symptoms are blended, expert decisions rely more on the intensely visible symptom and mainly on a single dimension, whereas the SOM/MLP classifier more accurately captures the average, variation and multi-dimensionality in data indicating that the model is more realistic and capable than the naked eye in detecting the disease.     

一种高精度微孔板浑浊度识别网络

提出一种基于卷积神经网络的高精度微孔板浑浊度分类算法。该算法主要将传统图像处理技术与卷积神经网络技术相结合,通过传统图像处理算法将圆孔从自然拍摄的微孔板图像中切割下来,并将切割下来的圆孔图像制作成圆孔数据集,用于网络模型的训练、评估和测试。同时,通过深度学习技术,设计并训练多个基于深度可分离卷积核的卷积神经网络模型,然后筛选出评估准确率最高的浑浊度分类模型,应用于圆孔识别系统,从而可提高研究人员的工作效率。     

Bone age estimation from carpal radiography images using deep learning

Bone age estimation has been used in medicine to verify whether the bone structure development degree of a person corresponds to their chronological age. Such estimate is useful for prognosis about the development of children and adolescents, as well as for the diagnosis of endocrinological diseases. This work proposes a fully automated methodology for bone age estimation from carpal radiography images. The methodology comprises two steps, the preprocessing of the image and the classification using a convolutional neural network. The system accuracy for different types of preprocessing is evaluated. We compare the accuracy achieved using the full radiography image as input for the neural network and using only parts of the image corresponding to the Phalangeal region, the Epiphyseal region, and the concatenation of these parts with a crop around the wrist. Digital image processing techniques are employed to segment these regions. Experiments are performed using radiography images from the California University Database. The impact of using different pre-trained neural networks for transfer learning is evaluated.     

A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification

As one of the most important algorithms in the field of deep learning technology, the convolutional neural network (CNN) has been successfully applied in many fields. CNNs can recognize objects in an image by considering morphology and structure rather than simply individual pixels. One advantage of CNNs is that they exhibit translational invariance; when an image contains a certain degree of distortion or shift, a CNN can still recognize the object in the image. However, this advantage becomes a disadvantage when CNNs are applied to pixel-based classification of remote-sensing images, because their translational invariance characteristics causes distortions in land-cover boundaries and outlines in the classification result image. This problem severely limits the application of CNNs in remote-sensing classification. To solve this problem, we propose a central-point-enhanced convolutional neural network (CE-CNN) to classify high-resolution remote-sensing images. By introducing the central-point-enhanced layer when classifying a sample, the CE-CNN increases the weight of the central point in feather maps while preserving the original textures and characteristics. In our experiment, we selected four representative positions on a high-resolution remote-sensing image to test the classification ability of the proposed method and compared the CE-CNN with the traditional multi-layer perceptron (MLP) and a traditional CNN. The results show that the proposed method can not only achieves a higher classification accuracy but also less distortion and fewer incorrect results at the boundaries of land covers. We further compared the CE-CNN with six state-of-the-art methods: k-NN, maximum likelihood, classification and regression tree (CART), MLP, support vector machine, and CNN. The results show that the CE-CNN’s classification accuracy is better than the other methods.     

Quaternion Fourier Descriptors for the Preprocessing and Recognition of Spoken Words Using Images of Spatiotemporal Representations

This paper presents an application of the quaternion Fourier transform for the preprocessing for neural-computing. In a new way the 1D acoustic signals of French spoken words are represented as 2D signals in the frequency and time domain. These kind of images are then convolved in the quaternion Fourier domain with a quaternion Gabor filter for the extraction of features. This approach allows to greatly reduce the dimension of the feature vector. Two methods of feature extraction are tested. The features vectors were used for the training of a simple MLP, a TDNN and a system of neural experts. The improvement in the classification rate of the neural network classifiers are very encouraging which amply justify the preprocessing in the quaternion frequency domain. This work also suggests the application of the quaternion Fourier transform for other image processing tasks.

Enhancing MLP networks using a distributed data representation

Multilayer perceptron (MLP) networks trained using backpropagation can be slow to converge in many instances. The primary reason for slow learning is the global nature of backpropagation. Another reason is the fact that a neuron in an MLP network functions as a hyperplane separator and is therefore inefficient when applied to classification problems in which decision boundaries are nonlinear. This paper presents a data representational approach that addresses these problems while operating within the framework of the familiar backpropagation model. We examine the use of receptors with overlapping receptive fields as a preprocessing technique for encoding inputs to MLP networks. The proposed data representation scheme, termed ensemble encoding, is shown to promote local learning and to provide enhanced nonlinear separability. Simulation results for well known problems in classification and time-series prediction indicate that the use of ensemble encoding can significantly reduce the time required to train MLP networks. Since the choice of representation for input data is independent of the learning algorithm and the functional form employed in the MLP model, nonlinear preprocessing of network inputs may be an attractive alternative for many MLP network applications.     

Automatic classification of computed tomography brain images using ANN,k-NN and SVM

Computed tomography images are widely used in the diagnosis of intracranial hematoma and hemorrhage. This paper presents a new approach for automated diagnosis based on classification of the normal and abnormal images of computed tomography. The computed tomography images used in the classification consists of non-enhanced computed tomography images. The proposed method consists of four stages namely pre-processing, feature extraction, feature reduction and classification. The discrete wavelet transform coefficients are the features extracted in this method. The essential coefficients are selected by the principal component analysis. The features derived are used to train the binary classifier, which infer automatically whether the image is that of a normal brain or a pathological brain, suffering from brain lesion. The proposed method has been evaluated on a dataset of 80 images. A classification with a success of 92, 97 and 98 % has been obtained by artificial neural network, k-nearest neighbor and support vector machine, respectively. This result shows that the proposed technique is robust and effective.     

神经网络在彩色图像压缩中的应用

文中旨在提出一种基于神经网络的图像压缩算法对彩色图像信息进行处理,从而减少大规模彩色图像的冗余度,方便其传输、存储及加密等。该算法通过将BP（Back Propagation）神经网络用于彩色图像压缩,利用其多层前馈网络的模式变化能力,实现了对由RGB编码得到的彩色图像数字矩阵进行的压缩编码。经Matlab仿真实验表明,该算法具有良好的压缩效果,且与灰度编码下的图像压缩结果对比,具有更好的压缩效率及保真效果,并能有效地保留原彩色图像的色彩信息,能够满足彩色图像压缩处理的要求。