An Improved Decision Support System for Detection of Lesions in Mammograms Using Differential Evolution Optimized Wavelet Neural Network

In this paper, a computerized scheme for automatic detection of cancerous lesion in mammograms is examined. Breast lesions in mammograms are an area with an abnormality or alteration in the breast tissues. Diagnosis of these lesions at the early stage is a very difficult task as the cancerous lesions are embedded in normal breast tissue structures. This paper proposes a supervised machine learning algorithm - Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of tumor masses in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture features of the abnormal breast tissues and normal breast tissues prior to classification. Then DEOWNN classifier is applied at the end to determine whether the given input data is normal or abnormal. The performance of the computerized decision support system is evaluated using a mini database from Mammographic Image Analysis Society (MIAS). The detection performance is evaluated using Receiver Operating Characteristic (ROC) curves. The result shows that the proposed algorithm has a sensitivity of 96.9% and specificity of 92.9%.     

基于模糊神经网络的乳腺微钙化点检测

在乳腺钼靶x线片计算机辅助诊断中,微钙化点的自动检测是难点问题之一.本文基于模糊神经网络,建议了一种乳腺微钙化点提取的新方法.算法首先利用随机方法产生大量的样本,经模糊判别后,输入神经网络进行训练,训练后的神经网络对感兴趣区域进行分类得到微钙化点.最后,仿真结果同其它算法进行比较,证明本文算法具有更高的阳性检出率。     

Classification of Retinal Damage by a Neural Network Based System

The objective of this research is to provide an ophthalmologist with a helpful system, capable of classifying a degree of patients' retinal hemorrhage. The system is composed of four modules: (a) data acquisition module, (b) image Database module, (c) image processing module, (d) image classification module. The system was trained with a modular neural network on a set of 25 images, and tested on a set of 160 images. A training performance of greater than 95% was achieved. The classifying part of the system showed 79% recognition accuracy. Since the testing images were taken from independent sources, we assume that the system should also provide an accurate classification of other image types.     

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。     

A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals

Epilepsy is a disorder of cortical excitability and still an important medical problem. The correct diagnosis of a patient’s epilepsy syndrome clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. The aim of this study is to evaluate epileptic patients and classify epilepsy groups such as partial and primary generalized epilepsy by using Radial Basis Function Neural Network (RBFNN) and Multilayer Perceptron Neural Network (MLPNNs). Four hundred eighteen patients with epilepsy diagnoses according to International League against Epilepsy (ILAE 1981) were included in this study. The correct classification of this data was performed by two expert neurologists before they were executed by neural networks. The neural networks were trained by the parameters obtained from the EEG signals and clinic properties of the patients. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. According to test results, RBFNN (total classification accuracy = 95.2%) has classified more successfully when compared with MLPNN (total classification accuracy = 89.2%). These results indicate that RBFNN model may be used in clinical studies as a decision support tool to confirm the classification of epilepsy groups after the model is developed.     

Automatic Classification of Heartbeats Using Wavelet Neural Network

The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WNN Classifier is developed. The experimental results obtained by testing the proposed approach on ECG data from the MIT-BIH arrhythmia database demonstrate the efficiency of such an approach when compared with other methods existing in the literature.     

An Artificial Neural Network Classification Approach for use the Ultrasound in Physiotherapy

In this study, a classification to be used in physiotherapy was realized by means of Artificial Neural Network (ANN). The aim of the classification was to determine the treatment length and appropriate ultrasound value for the age of physiotherapy patients, the area on which ultrasound will be applied, the fat rate in tissue and related factors. For this purpose, the patient information obtained from Selçuk University, Meram School of Medicine Hospital, Physiotherapy Department was used. In order to identify the appropriate ultrasound value and treatment length for the patient, the ultrasound therapy device realized with ANN was placed together in an embedded system. The results obtained by means of the designed and realized embedded system were compared with data gathered from an expert. As a result, the data obtained from the designed system were found out to be in line with the existing data.     

模糊遗传人工神经网络算法提取乳腺微钙化点的效果

 【目的】微钙化点是早期乳腺癌的重要征象之一，本研究联合运用遗传算法、模糊数学和人工神经网络，建议一种乳腺微钙化点提取的新方法，为乳腺病变的自动识别提供前期处理，为早期乳腺癌的临床诊断提供帮助。【方法】首先利用随机方法产生大量的样本，然后，利用模糊遗传算法对产生的随机样本进行分类，将分类后的样本输入人工神经网络进行训练，将310幅乳腺图像的感兴趣区域输入训练后的人工神经网络分类器进行分类。【结果】与微钙化点提取方面的同类文献相比较，结果表明该算法在相同误检率下得到较高的阳性检出率。【结论】研究表明综合运用遗传算法、模糊数学和人工神经网络进行乳腺微钙化点提取比单纯运用人工神经网络提取效果好。     

Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis

Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.     

量子神经网络在心电图分类中的应用

将量子叠加的概念引入前向神经网络,提出了量子神经网络的计算模型。量子神经网络分类器是将量子迁移（量子间隔）概念引入前向神经网络,在隐含层和输出层借鉴量子理论中的量子迁移（量子间隔）思想,神经元采用多个激励函数的叠加,形成对特征空间的多级划分,在训练过程中,量子神经元能够根据需要伸展或坍塌。当输入模糊信息时,该算法可以学习数据集中的不精确性或不确定性,具有较高的分类精度。将该算法应用于心电图诊断中,结果表明具有较好的分类效果和较快的训练速度。     

A Method for Microcalcifications Detection in Breast Mammograms

Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation. Then unsharp masking and histogram modification technique has used to enhance the contrast of the image and to clarify some details like microcalcifications. Lastly, Discrete Wavelets Transform applied for detecting the abnormality. The proposed method has evaluated using the Mammographic Image Analysis Society (AS) mammography databases. The proposed method has achieved acceptable results.     

A Radial Basis Function Neural Network (RBFNN) Approach for Structural Classification of Thyroid Diseases

The thyroid is a gland that controls key functions of body. Diseases of the thyroid gland can adversely affect nearly every organ in human body. The correct diagnosis of a patient’s thyroid disease clarifies the choice of drug treatment and also allows an accurate assessment of prognosis in many cases. This study investigates Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function Neural Network (RBFNN) for structural classification of thyroid diseases. A data set for 487 patients having thyroid disease is used to build, train and test the corresponding neural networks. The structural classification of this data set was performed by two expert physicians before the input variables and results were fed into the neural networks. Experimental results show that the predictions of both neural network models are very satisfying for learning data sets. Regarding the evaluation data, the trained RBFNN model outperforms the corresponding MLPNN model. This study demonstrates the strong utility of an artificial neural network model for structural classification of thyroid diseases.     

A Neural Network Application to Classification of Health Status of HIV/AIDS Patients

This paper presents an application of neural networks to classify and to predict the health status of HIV/AIDS patients. A neural network model in classifying both the well and not-well health status of HIV/AIDS patients is developed and evaluated in terms of validity and reliability of the test. Several different neural network topologies are applied to AIDS Cost and Utilization Survey (ACSUS) datasets in order to demonstrate the neural network's capability.     

The Classification of Obesity Disease in Logistic Regression and Neural Network Methods

The aim of this study is to establish an automated system to recognize and to follow-up obesity. In this study, the areas affected from obesity were examined with a classification considering the divergent arteries and body mass index of 30 healthy and 52 obese people by using two different mathematical models such as the traditional statistical method based on logistic regression and a multi-layer perception (MLP) neural network, and then classifying performances of logistic regression and neural network were compared. As a result of this comparison, it is observed that the classifying performance of neural network is better than logistic regression; also the reasons of this result were examined. Furthermore, after these classifications it is observed that in obesity the body mass index is more affected than the divergent arteries.     

Classification of MCA Stenosis in Diabetes by MLP and RBF Neural Network

For the classification of Middle Cerebral Artery (MCA) stenosis, Doppler signals have been received from the diabetes and control group by using 2 MHz Transcranial Doppler. After the Fast Fourier Transform (FFT) analyses of the Doppler signals, Power Spectrum Density (PSD) estimations have been made and Multilayer Perceptron (MLP) and Radial Basis Function (RBF) have been dealt to apply to the neural networks. PSD estimations of Doppler signals received from MCA of 104 subjects have been successfully classified by MLP (correct classification = 94.2%) and RBF (correct classification = 88.4%) neural network. As we have seen in the area under ROC curve (AUC), MLP neural network (AUC = 0.934) has classified more successfully when compared with RBF neural network (AUC = 0.873).     

Neural Network in Communication with Medical Computer System

The paper presents a concept of an experimental module designed to recognize spoken utterances that cover a limited range of words indispensable in dialogs with computer medical systems. Research into the recognition of spoken words by a module based on artificial neural network is described. Usefulness of the obtained results for surgery-assisting multimedia systems and for a patient simulator supporting medical education of students in case history-taking and diagnosing is also discussed.     

Breast Cancer Classification Based on Advanced Multi Dimensional Fuzzy Neural Network

Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as ??curse of dimensionality??. The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.     

A Classification System for Stenosis from Mitral Valve Doppler Signals Using Adaptive Network based Fuzzy Inference System

In this study, cardiac Doppler parameters were studied in 60 patients with mitral valve stenosis and compared with 41 ages and sex matched healthy controls. Firstly, the sonograms which represent the changes in Doppler frequency with respect to time were performed from mitral valve Doppler signals using short time Fourier transformation (STFT) method. Secondly, the envelopes of these sonograms and data set depicted from sonogram envelopes were acquired. Finally, the processed data set are applied to the proposed adaptive network based fuzzy inference system (ANFIS) model has potential in classifying the mitral valve Doppler signals. This result confirms that our technique contribute to the detection of mitral valve stenosis and our method offers more reliable information than looking at the sonogram on the Doppler screen and making a decision from the visual inspection. The proposed ANFIS model combined the neural network with adaptive capabilities and qualitative approach of fuzzy logic. The obtained results show that 98% correct classification was achieved, whereas two false classifications have been observed for the test group of 101 people.     

2型糖尿病患者亚临床动脉粥样硬化的多层人工神经网络分类预测模型的构建

背景　现阶段我国2型糖尿病（T2DM）患者数量较多,亟须开发简单、有效的亚临床动脉粥样硬化发生风险评估工具。目的　依据多项指标构建预测T2DM患者亚临床动脉粥样硬化的多层人工神经网络分类模型并验证其预测准确性。方法　选取2010年1月至2016年12月在安徽医科大学第三附属医院住院的T2DM患者3 627例,均行双侧颈动脉彩色多普勒超声检查,其中检出亚临床动脉粥样硬化者2 196例（观察组）,未检出亚临床动脉粥样硬化者1 431例（对照组）。比较两组患者一般资料、实验室检查指标及脂肪肝发生情况并据此构建多层人工神经网络分类模型。从3 627例T2DM患者中随机选取3 027例患者作为训练集,其余600例患者作为测试集,验证多层人工神经网络分类模型的预测准确性。结果　两组患者体质指数、舒张压、有吸烟史者所占比例、有饮酒史者所占比例、饮酒量、直接胆红素、总蛋白、天冬氨酸氨基转移酶、血尿酸、三酰甘油、低密度脂蛋白胆固醇/高密度脂蛋白胆固醇比值、促甲状腺激素、游离三碘甲状腺原氨酸、游离甲状腺素、糖化血红蛋白、空腹血糖、空腹C肽、HOMA-C肽指数、严重脂肪肝所占比例比较,差异无统计学意义（P>0.05）;观察组患者女性所占比例、收缩压、有高血压病史者所占比例、球蛋白、总胆汁酸、尿素氮、血肌酐、胱抑素C、尿微量白蛋白排泄率、总胆固醇、低密度脂蛋白胆固醇、高密度脂蛋白胆固醇、白细胞计数、中性粒细胞计数、糖化血红蛋白、空腹血糖高于对照组,年龄、吸烟量大于对照组,病程、吸烟时间、饮酒时间长于对照组,有糖尿病家族史者所占比例、总胆红素、间接胆红素、白蛋白、丙氨酸氨基转移酶、肾小球滤过率、三酰甘油/高密度脂蛋白胆固醇比值、淋巴细胞计数、红细胞计数、血红蛋白、脂肪肝发生率低于对照组（P<0.05）。结合临床实际,将上述49项指标作为输入变量构建多层人工神经网络分类模型;在测试集上,Logistic模型预测T2DM患者亚临床动脉粥样硬化的准确率为59%,而多层人工神经网络分类模型隐藏层数为3时预测T2DM患者亚临床动脉粥样硬化的准确率为76%。结论　本研究构建的多层人工神经网络分类模型对T2DM患者亚临床动脉粥样硬化的预测准确率较高,可作为T2DM患者亚临床动脉粥样硬化发生风险评估工具。     

E-Nose System for Anesthetic Dose Level Detection using Artificial Neural Network

In this study, an E-Nose system was realized for the anesthetic dose level prediction. For this purpose, sevoflurane anesthetic agent was measured using the E-Nose system implemented with sensor array of quartz crystal microbalances (QCM). In surgeries, anesthetic agents are given to the patients with carrier gases of oxygen (O₂) and nitrous oxide (N₂O). Frequency changes on QCM sensors to the eight sevoflurane anesthetic dose levels were recorded via RS-232 serial port. A multilayer feed forward artificial neural network (MLNN) structure was used to provide the relationship between the frequency change and the anesthetic dose level. The MLNNs were trained with the measured data using Levenberg–Marquardt algorithm. Then, the trained MLNNs were tested with random data. The results have showed that, acceptable anesthetic dose level predictions have been obtained successfully.