Application of Artificial Neural Networks to Medical Image Pattern Recognition: Detection of Clustered Microcalcifications on Mammograms and Lung Cancer on Chest Radiographs

Three neural network models were employed to evaluate their performances in the recognition of medical image patterns associated with lung cancer and breast cancer in radiography. The first method was a pattern match neural network. The second was a conventional backpropagation neural network. The third method was a backpropagation trained neocognitron in which the signal propagation is operated with the convolution calculation from one layer to the next. In the convolution neural network (CNN) experiment, several output association methods and trainer imposed driving functions in conjunction with the convolution neural network are proposed for general medical image pattern recognition. An unconventional method of applying rotation and shift invariance is also used to enhance the performance of the neural nets.We have tested these methods for the detection of microcalcifications on mammograms and lung nodules on chest radiographs. Pre-scan methods were previously described in our early publications. The artificial neural networks act as final detection classifiers to determine if a disease pattern is presented on the suspected image area. We found that the convolution neural network, which internally performs feature extraction and classification, achieves the best performance among the three neural network models. These results show that some processing associated with disease feature extraction is a necessary step before a classifier can make an accurate determination.     

Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble

Comprehensibility is very important when machine learning techniques are used in computer-aided medical diagnosis. Since an artificial neural network ensemble is composed of multiple artificial neural networks, its comprehensibility is worse than that of a single artificial neural network. In this paper, C4.5 Rule-PANE, which combines an artificial neural network ensemble with rule induction by regarding the former as a preprocess of the latter, is proposed. At first, an artificial neural network ensemble is trained. Then, a new training data set is generated by feeding the feature vectors of original training instances to the trained ensemble and replacing the expected class labels of original training instances with the class labels output from the ensemble. Additional training data may also be appended by randomly generating feature vectors and combining them with their corresponding class labels output from the ensemble. Finally, a specific rule induction approach, i.e., C4.5 Rule, is used to learn rules from the new training data set. Case studies on diabetes, hepatitis , and breast cancer show that C4.5 Rule-PANE could generate rules with strong generalization ability, which benefits from an artificial neural network ensemble, and strong comprehensibility, which benefits from rule induction.     

基于BP＿Adaboost模型的乳腺癌诊断预测方法研究

乳腺癌是全球女性发病率位居首位的恶性肿瘤,研究基于神经网络模型的乳腺癌诊断预测方法的目的是将临床与机器学习相结合,有助于医疗工作者更加快速准确地判断出患病与否,同时解决现有模型中存在的过拟合以及漏诊率和误诊率过高的问题,并提高预测模型的准确率。本文采用加州大学欧文分校(UCI)数据集,共669个样本,其中包含357个良性样本和212个恶性肿瘤样本,共计10个特征训练预测模型。将10个神经网络模型采用Adaboost方法相结合,即通过Adaboost算法组合多个弱分类器从而形成一个强分类器,最终输出一个具有更高准确率、有较强的自学习能力、自适应能力且泛化性能优良的集成预测模型。结论表明,该模型的预测准确率达到98.5507%,同时准确率(AUC)为0.9966,说明所建模型区分度较好,可以反映模型的诊断价值,且非常稳定,具有非常好的验证效果,为临床应用提供进一步的技术支持和保障。     

高光谱成像技术检测冷却羊肉表面细菌总数

为了对冷却羊肉表面细菌总数进行无损检测,采用不同波段范围高光谱成像系统结合多种建模方法建立预测模型,进行理论分析和实验验证。分别在400nm~110nm和900nm~1700nm波长范围内获取冷却羊肉样本的高光谱图像信息,结合偏最小二乘和人工神经网络(反向人工神经网络和径向基人工神经网络)建立预测模型。结果表明,神经网络建模效果优于偏最小二乘;其中,径向基人工神经网络模型在400nm~1100nm和900nm~1700nm波长范围内相关系数分别为0.9872和0.9988,均方根误差分别为0.8210和0.2507,预测效果最好;而900nm~1700nm波长范围为最佳建模波长。这一结果说明利用高光谱图像技术对冷却羊肉表面细菌总数进行快速无损检测是可行的。     

Use of Novel Thermography Features of Extraction and Different Artificial Neural Network Algorithms in Breast Cancer Screening

The breast thermography process is a physiological investigation that gives data dependent on the heat variations in the breast. It accounts for the heat circulation of a body utilizing the infrared radiation produced by the outside of that body. Precancerous tissue and the zone around a carcinogenic tumor have greater heats because of angiogenesis, and higher substance and blood vein action than a healthy breast; consequently, breast thermography can possibly recognize early strange changes in breast tissues. Thermography can identify the earliest indication of cancer initiation before mammography can notice. For the extraction of the scarce data from the breast, features like Energy, Effective information, Multi quadratic, Sigmoid, and Age of the patient are determined and applied to the neural network as inputs. Resilient backpropagation algorithm (RBPA) is a worldwide methodology managing weights; it is hard to get better subtleties from the breast image. To overcome the problems of RBPA, the artificial neural network (ANN) classifier is being used as a new derived Extension of Resilient backpropagation algorithm (ERBPA) for validation purposes. In this paper three ANN-based algorithms are used: Gradient descent, RBPA, and ERBPA are discussed and compared. As per the outcomes, the recently determined ERBPA is a progressively exact methodology to classify benign and malignant pathology. An accuracy of 99.90% has been obtained to bring about an effective strategy, which can recognize and cure breast cancer at an early stage.

     

基于临床路径的病例组合方法研究

目的:探讨基于临床路径的病例组合方法和人工神经网络在病例组合中的应用。方法:利用某综合性医院的一个临床路径流程下的5 2 3份出院病例资料,采用K -MODES聚类方法进行组合,用神经网络对预测病例的病例组合进行判断。结果5 2 3份病例聚为4组,各组间费用95 %可信区间互不重合;神经网络的训练误差为0 .0 0 2 9,病例组合预测和判断符合率为98.91%。结论:以临床路径下产生的病例为单元样本进行病例组合,结果更科学、客观。神经网络用于病例组合判断,不用确定单个节点变量的分割值,更符合病例组合由多变量共同作用的实情。     

人工神经网络非线性背景下物联网技术社区时序预测的应用研究

社区时序数据建模是世界各国的学者研究的新型热点课题,人工神经网络环境下复杂非线性物联网技术社区时序数据系统得到了海量实践的应用。本课题对非线性物联网技术社区时序数据预测神经网络中存在的几个瓶颈进行分析探讨,基于提出人工神经网络非线性视角下物联网技术社区时序数据预测中的应用研究来优化预测神经网络环境下中的瓶颈。因此,通过人工神经网络社区数据的仿真实验表明该算法的高效性和实用性。     

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.     

Artificial convolution neural network techniques and applications for lung nodule detection

We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.     

神经网络在时间序列预测中的应用研究

介绍了时间序列预测的基本概念、各种模型,分析了基于神经网络的时间序列预测方法,阐述了BP神经网络基本原理,提出了一种基于BP神经网络的时间序列的预测和方法。通过应用实例的分析表明,以此方法得到BP网络应用于非线性时间序列预测是可行的,神经网络方法可以成功地用于分析预测时间序列变量。     

基于人眼视觉特性的深度学习全参考图像质量评价方法

针对现有的图像质量评价方法普遍为人工设计特征,难以自动且有效提取到符合人类视觉系统的图像特征,受人眼视觉特性的启发,提出一种新的基于卷积神经网络的全参考图像质量评价方法（DeepFR）。该方法基于对数据集本身的学习设计了卷积神经网络DeepFR模型,利用人眼视觉系统对梯度的敏感性进行加权优化,提取了符合人眼视觉特性的视觉感知图。实验表明:设计的DeepFR模型优于已有的全参考图像质量评价方法,其预测结果与主观质量评价有很好的精确性与一致性。     

基于神经网络的传播预测模型浅析

传播预测模型是网络规划和频谱资源合理利用的基础。在分析了现有模型不足的基础上,介绍了人工神经网络的结构及其传播预测模型的构建,并对该模型进行了改进,提出了应用反向传播(Back-Propagation,BP)神经网络的混合传播模型。人工神经网络具有良好的非线性逼近能力和泛化能力,非常适用于特定地区传播损耗的预测。通过对试验数据的分析处理,验证了此方法能够更真实地反映该区域的无线电波传播环境,得到更高的预测准确度。     

Research onconvolutional neural network for reservoir parameter prediction

As the branch of artificial intelligence,artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However,they cannot learn the feature from networks.In recent years,deep learning becomes more and more advanced,but the research on the field of geological reservoir pa-rameter prediction is still rare.A method to predict reservoir parameters by convolutional neural network was presented,which can not only predict reservoir parameters accurately,but also get features of the geological reservoir.The study es-tablished the convolutional neural network model.Results show that the convolutional neural network can be used for reservoir parameter prediction,and get high prediction precision.Moreover,convolutional features from convolutional neural network provided important support for geological modeling and logging interpretation.     

基于模糊神经网络智能预测模型的设计与实现

针对智能决策支持系统中经常遇到的预测类问题,根据人工神经网络和模糊逻辑系统的各自特点,设计一种模糊神经网络模型,将模糊系统用类似于神经网络的结构表示,再用相应的学习算法训练模糊系统实现模糊推理.并对此模型进行预测验证和编程实现.     

CNN Based No-Reference HDR Image Quality Assessment

Motivated by the problems of non-universality and over-reliance on the original reference image in High dynamic range (HDR) Image quality assessment (IQA), a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed. The Salience detection by self-resemblance (SDSR) algorithm which extracts the salient regions of the HDR image, is used to simulate the human visual attention mechanism. Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity. And this network consists of an Error estimation network (Error-net), a Perceptual resistance network (PR-net) and a mixing function. The experimental results indicate that the method proposed has high consistency with subjective perception, and the value of assessment metrics Spearman rank-order correlation coefficient (SROCC), Pearson product-moment correlation coefficient (PLCC) and Root mean square error (RMSE) correspondingly reaches 0.941, 0.910 and 8.176 as well. It is comparable with classic full-reference HDR IQA methods.     

Analysis of mammographic microcalcifications using gray-level image structure features

At present, mammography associated with clinical breast examination and breast self-examination is the only effective and viable method for mass breast screening. The presence of microcalcifications is one of the primary signs of breast cancer. It is, difficult however, to distinguish between benign and malignant microcalcifications associated with breast cancer. Here, the authors define a set of image structure features for classification of malignancy. Two categories of correlated gray-level image structure features are defined for classification of "difficult-to-diagnose" cases. The first category of features includes second-order histogram statistics-based features representing the global texture and the wavelet decomposition-based features representing the local texture of the microcalcification area of interest. The second category of features represents the first-order gray-level histogram-based statistics of the segmented microcalcification regions and the size, number, and distance features of the segmented microcalcification cluster. Various features in each category were correlated with the biopsy examination results of 191 "difficult-to-diagnose" cases for selection of the best set of features representing the complete gray-level image structure information. The selection of the best features was performed using the multivariate cluster analysis as well as a genetic algorithm (GA)-based search method. The selected features were used for classification using backpropagation neural network and parameteric statistical classifiers. Receiver operating characteristic (ROC) analysis was performed to compare the neural network-based classification with linear and k-nearest neighbor (KNN) classifiers. The neural network classifier yielded better results using the combined set of features selected through the GA-based search method for classification of "difficult-to-diagnose" microcalcifications.     

Dual‐Phase Approach to Improve Prediction of Heart Disease in Mobile Environment

In this paper, we propose a dual‐phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease — in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self‐organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.     

Image quality assessment based on self-supervised learning and knowledge distillation

Deep neural networks have achieved great success in a wide range of machine learning tasks due to their excellent ability to learn rich semantic features from high-dimensional data. Deeper networks have been successful in the field of image quality assessment to improve the performance of image quality assessment models. The success of deep neural networks majorly comes along with both big models with hundreds of millions of parameters and the availability of numerous annotated datasets. However, the lack of large-scale labeled data leads to the problems of over-fitting and poor generalization of deep learning models. Besides, these models are huge in size, demanding heavy computation power and failing to be deployed on edge devices. To deal with the challenge, we propose an image quality assessment based on self-supervised learning and knowledge distillation. First, the self-supervised learning of soft target prediction given by the teacher network is carried out, and then the student network is jointly trained to use soft target and label on knowledge distillation. Experiments on five benchmark databases show that the proposed method is superior to the teacher network and even outperform the state-of-the-art strategies. Furthermore, the scale of our model is much smaller than the teacher model and can be deployed in edge devices for smooth inference.     

BP神经网络的发展现状综述

讨论目前人工神经网络领域中BP神经网络的特点、改进算法以及在实际中的应用。主要包括模式识别及分类、故障智能诊断、图像处理、函数拟合、最优预测等方面的应用。最后对目前人工神经网络的存在问题和发展前景做了初步探讨。     

人工神经网络在决策支持系统中的应用

在传统的决策支持系统中，加入基于神经网络的预测模块，完成了复杂的非线性预测，使决策支持系统更加的智能化、自动化。该模块采用反向传输BP神经网络模型来实现，通过网络的自适应训练和学习，找出输入和输出之间关系以求解问题。利用该系统对社会消费品零售额进行预测，结果表明该系统具有较强的实用性和通用性。