Tuberculosis Disease Diagnosis Using Artificial Neural Networks

Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital’s database using patient’s epicrisis reports.     

A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm

In this study, a hepatitis disease diagnosis study was realized using neural network structure. For this purpose, a multilayer neural network structure was used. Levenberg–Marquardt algorithm was used as training algorithm for the weights update of the neural network. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database. We obtained a classification accuracy of 91.87% via tenfold cross validation.     

Classifying Epilepsy Diseases Using Artificial Neural Networks and Genetic Algorithm

In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.     

A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network

Hepatitis is a major public health problem all around the world. Hepatitis disease diagnosis via proper interpretation of the hepatitis data is an important classification problem. In this study, a comparative hepatitis disease diagnosis study was realized. For this purpose, a probabilistic neural network structure was used. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database.     

基于人工神经网络的冠心病鉴别诊断方

人工神经网络在冠心病诊断领域已取得广泛应用并取得良好效果,但其在冠心病鉴别诊断领域的应用仍为空白。本文从冠心病的鉴别诊断入手,选用基于LM算法的人工神经网络,结合目前中国医疗场所对冠心病及其他疾病的诊断方法,就如何运用人工神经网络实现冠心病的鉴别诊断进行了理论上的探讨,并给出了具体的样本信息数字化方法,填补了国内相关领域研究的空白。     

A New Method for Diagnosis of Cirrhosis Disease: Complex-valued Artificial Neural Network

In this study, complex-valued artificial neural network (CVANN) that is a new technique for biomedical pattern classification was proposed for classifying portal vein Doppler signals recorded from 54 patients with cirrhosis and 36 healthy subjects. Fast Fourier transform values of Doppler signals were calculated for pre-processing and obtained values, which include real and imaginary components, were used as the inputs of the CVANN for classification of Doppler signals. Classification results of CVANN show that Doppler signals were classified successfully with 100% correct classification rate using leave-one-out cross-validation. Besides, CVANN has 100% sensitivity and 100% specificity. These results were found to be compliant with the expected results that are derived from physician’s direct diagnosis. This method would be to assist the physician to make the final decision.     

应用人工神经网络技术诊断涂阴肺结核的研究

目的研究人工神经网络在涂阴肺结核诊断中的应用。方法将全部研究对象随机分为建模样本和证实样本,再将建模样本分为训练样本及校验样本。利用训练样本筛选出对诊断涂阴肺结核有意义的单项参数指标来构建人工神经网络诊断模型,并用校验样本确定合适的网络结构,最后用证实样本评价其泛化能力。结果得到人工神经网络模型结构为(29-9-1)-BP型。该模型在证实样本ROC曲线下面积为(0.989±0.015),诊断准确率为93.10%,灵敏度及特异度分别为88.89%及100%。结论(29-9-1)-BP型网络模型可作为涂阴肺结核的诊断工具,有良好的泛化能力,值得进一步探讨。     

改进的粒子群优化算法结合人工神经网络用于有机物毒性的QSAR研究

介绍了粒子群优化算法和Alopex-B算法的基本原理,提出了一种用Alopex-B算法改进的粒子群优化算法,并将其应用于函数优化和有机物毒性的QSAR研究。结果表明:改进型粒子群算法对复杂的测试函数搜索效率明显提高,应用于有机物毒性的QSAR研究能提高计算的精确度,降低预测误差。     

诊断推理中人工神经网络与基于案例推理的结合

对基于人工神经网的诊断方法与基于案例推理的方法(Case-Based Reasoning, CBR)的结合进行了研究,提出了两种结合方案.针对CBR系统建立案例库索引这一难点,方案一利用人工神经网诊断分类器的诊断结果对案例库进行索引;方案二用人工神经网为待诊断对象建立模型,对正常的状态作出预测,通过预测值与实际测量值的差异对案例库进行索引.在作出最后诊断之前两种方案都利用CBR的推理结果对神经网的诊断结果进行检验和修正,从而给出更为精确、便于解释的诊断结果.经过实验对比验证,人工神经网与CBR方法的结合有效的弥补了它们在诊断推理应用中通常存在的局限.从诊断准确率、诊断速度以及诊断系统的自学习性等方面,都取得了优于传统人工神经网方法和CBR方法的性能,较好的完成了诊断推理工作.     

基于混合编码的遗传算法在神经网络优化中的应用

提出了一种结合浮点数编码与二进制编码的混合编码遗传算法,该算法在同一条染色体上同时使用浮点数编码与二进制编码,有机结合了两者的优点,并与BP算法结合用于优化神经网络的结构和系数,获得具有更好泛化能力的神经网络.仿真实验结果证实了这种混合编码遗传算法的有效性和优越性能.     

Predicting Arterial Blood Gas Values from Venous Samples in Patients with Acute Exacerbation Chronic Obstructive Pulmonary Disease Using Artificial Neural Network

Arterial blood gas (ABG) has an important role in the clinical assessment of patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Because of ABG complications, an alternative method is beneficial. We have trained and tested five artificial neural networks (ANNs) with venous blood gas (VBG) values (pH, PCO₂, HCO₃, PO₂, and O₂ saturation) as inputs, to predict ABG values in patients with AECOPD. Venous and arterial blood samples were collected from 132 patients. Using the data of 106 patients, the ANNs were trained and validated by back-propagation algorithm. Subsequently, data from the remainder 26 patients was used for testing the networks. The ability of ANNs to predict ABG values and to detect significant hypercarbia was assessed and the results were compared with a linear regression model. Our results indicate that the ANNs provide an accurate method for predicting ABG values from VBG values and detecting hypercarbia in AECOPD.     

Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.     

Combining Neural Network and Genetic Algorithm for Prediction of Lung Sounds

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks–genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of 15–20 s were sampled from subjects. From each interval, full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated by the spectral analysis are selected by genetic algorithm and applied to neural network. Multilayer perceptron (MLP) neural network employing backpropagation training algorithm was used to predict the presence or absence of adventitious sounds (wheeze and crackle). We used genetic algorithms to search for optimal structure and training parameters of neural network for a better predicting of lung sounds. This application resulted in designing of optimum network structure and, hence reducing the processing load and time.     

遗传算法优化BP神经网络的老年痴呆症智能诊断

为了提高BP神经网络对疾病诊断的效率和预测准确率,提出一种遗传算法优化BP神经网络的老年痴呆症智能诊断模型,并以医院电子病历数据挖掘为例,对老年痴呆症诊断建立预测模型。该方法首先利用遗传算法的搜索寻优技术进行特征约简,然后将约简后的特征作为BP神经网络的输入变量,训练和构建BP神经网络模型。仿真实验在Matlab软件平台上进行,结果表明：与单BP神经网络相比,遗传算法优化BP神经网络能够降低模型训练时间、提高预测精度,是一种切实可行的老年痴呆症辅助诊断方法。     

Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network

Disease diagnosis from medical images has become increasingly important in medical science. Abnormality identification in retinal images has become a challenging task in medical science. Effective machine learning and soft computing methods should be used to facilitate Diabetic Retinopathy Diagnosis from Retinal Images. Artificial Neural Networks are widely preferred for Diabetic Retinopathy Diagnosis from Retinal Images. It was observed that the conventional neural networks especially the Hopfield Neural Network (HNN) may be inaccurate due to the weight values are not adjusted in the training process. This paper presents a new Modified Hopfield Neural Network (MHNN) for abnormality classification from human retinal images. It relies on the idea that both weight values and output values can be adjusted simultaneously. The novelty of the proposed method lies in the training algorithm. In conventional method, the weights remain fixed but the weights are changing in the proposed method. Experimental performed on the Lotus Eye Care Hospital containing 540 images collected showed that the proposed MHNN yields an average sensitivity and specificity of 0.99 and accuracy of 99.25%. The proposed MHNN is better than HNN and other neural network approaches for Diabetic Retinopathy Diagnosis from Retinal Images.     

Using an Artificial Neural Network to Define the Planning Target Volume in Radiotherapy

A neural network for predicting the planning target volume in radiotherapy from the shape of the detected tumor is designed and tested in this research project. The proposed neural network is able to generalize expert medical knowledge and predict the planning target volume from a three-dimensional image of the detected tumor. Initial results for simple shaped brain tumors are presented in this paper.     

A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks

Chronic Obstructive Pulmonary Disease (COPD) is a disease state characterized by airflow limitation that is not fully reversible. The airflow limitation is usually both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases. COPD is important health problem and one of the most common illnesses in Turkey. It is generally accepted that cigarette smoking is the most important risk factor and genetic factors are believed to play a role in the individual susceptibility. In this study, a study on COPD diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. Back propagation with momentum and Levenberg–Marquardt algorithms were used for the training of the neural networks. The COPD dataset were prepared from a chest diseases hospital’s database using patient’s epicrisis reports.     

人工神经网络在中医诊断信息化中的应用

中医学是一个非常复杂的系统,临床证候之间、临床证候与诊断目标之间、临床证候与方药之间的关系具有非线性、复杂性、模糊性、非定量的特点.人工神经网络能从海量数据中提取隐含的有意义的知识,能模拟这种非线性映射关系,建立诊断、判别模型,做出前瞻性决策,正是这种优势使得人工神经网络技术有可能为解决中医脉象辨识信息化、中医舌象辨识信息化、中医证候辨识信息化中权值难以明确的问题提供更为科学的方法与途径.     

Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network

Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decades, because it is a non-invasive, easy to apply and reliable technique. In this study, a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) has been developed in order to classify the internal carotid artery Doppler signals obtained from the 191 subjects, 136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subject. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, power spectral density (PSD) estimates of internal carotid artery Doppler signals were obtained by using Burg autoregressive (AR) spectrum analysis technique in order to obtain medical information. In the classification stage, LVQ NN was used classify features from Burg AR method. In experiments, LVQ NN based method reached 97.91% classification accuracy with 5 fold Cross Validation (CV) technique. In addition, the classification performance of the LVQ NN was compared with some methods such as Multi Layer Perceptron (MLP) NN, Naive Bayes (NB), K-Nearest Neighbor (KNN), decision tree and Support Vector Machine (SVM) with sensitivity and specificity statistical parameters. The classification results showed that the LVQ NN method is effective for classification of internal carotid artery Doppler signals.     

An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs

Determining the cause of seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.