Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

Compatible abnormality detection technique for CT and MRI brain images

Abstract

An automated computerised tomography (CT) and magnetic resonance imaging (MRI) brain images are used to perform an efficient classification. The proposed technique consists of three stages, namely, pre-processing, feature extraction and classification. Initially, pre-processing is performed to remove the noise from the medical MRI images. Then, in the feature extraction stage, the features that are related with MRI and CT images are extracted and these extracted features which are given to the Feed Forward Back-propagation Neural Network (FFBNN) is exploited in order to classify the brain MRI and CT images into two types: normal and abnormal. The FFBNN is well trained by the extracted features and uses the unknown medical brain MRI images for classification in order to achieve better classification performance. The proposed method is validated by various MRI and CT scan images. A classification with an accomplishment of 96% and 70% has been obtained by the proposed FFBNN classifier. This achievement shows the effectiveness of the proposed brain image classification technique when compared with other recent research works.     

基于离散小波变换和Kalman滤波的直升机主减智能状态预测

主减速器（简称“主减”）是直升机传动系统的关键部件,它常处于高转速高负荷的恶劣环境下,对其运行状态进行预测,于直升机的安全性来说至关重要。鉴于此,提出了一种离散小波变换（DWT）、Kalman滤波以及Elman神经网络相结合的直升机主减智能状态预测系统：DWT使用“db44”母小波对振动信号进行分解提取特征向量,Kalman滤波对未来各时刻的特征向量进行预测,Elman神经网络对预测值进行故障辨识和分类。在Kalman滤波算法中,提出了一种新的预测算法,并用实验对该算法组成的系统进行验证,结果表明：该 Kalman滤波算法预测效果好,更适用于对主减的特征向量进行预测;离散小波变换（DWT）、Kalman滤波以及Elman神经网络相结合组成的智能状态预测系统是可行的,它能很好地对主减的未来状态进行预测。     

Detection of brain tumour from MRI image using modified region growing and neural network

Abstract

Region growing is an important application of image segmentation in medical research for detection of tumour. In this paper, we propose an effective modified region growing technique for detection of brain tumour. It consists of four steps which includes: (i) pre-processing; (2) modified region growing by the inclusion of an additional orientation constraint in addition to the normal intensity constrain; (3) feature extraction of the region; and (4) final classification using the neural network. The performance of the proposed technique is systematically evaluated using the magnetic resonance imaging (MRI) brain images received from the public sources. For validating the effectiveness of the modified region growing, we have considered the quantity rate parameter. For the evaluation of the proposed technique of tumour detection, we make use of sensitivity, specificity and accuracy values which we compute from finding out false positive, false negative, true positive and true negative. Comparative analyses were made of the normal and the modified region growing using both the Feed Forward Neural Network (FFNN) and Radial Basis Function (RBF) neural network. From the results obtained, we could see that the proposed technique achieved the accuracy of 80% for the testing dataset, which clearly demonstrated the effectiveness of the modified region growing when compared to the normal technique.     

Computational intelligence techniques for human brain MRI classification

This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K‐Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg‐Marquardt (LM‐ANN). Classification accuracy of 100% for KNN and LM‐ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.     

A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model. To handle the classification process, we have collected a total number of 2043 MRI patients of normal, benign, and malignant tumor. Three model CNN, multi-level CNN, and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors. All the model results are calculated in terms of various numerical values identified as precision (P), recall (R), accuracy (Acc) and f1-score (F1-S). The obtained average results are much better as compared to already existing methods. This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.     

基于卷积神经网络的生物式水质监测方法

生物式水质监测通常是先通过提取水生物在不同环境下的应激反应特征,再进行特征分类,从而识别水质。针对水质监测问题,提出一种使用卷积神经网络(CNN)的方法。鱼类运动轨迹是当前所有文献使用的多种水质分类特征的综合性表现,是生物式水质分类的重要依据。使用Mask-RCNN的图像分割方法,求取鱼体的质心坐标,并绘制出一定时间段内鱼体的运动轨迹图像,制作正常与异常水质下两种轨迹图像数据集。融合Inception-v3网络作为数据集的特征预处理部分,重新建立卷积神经网络对Inception-v3网络提取的特征进行分类。通过设置多组平行实验,在不同的水质环境中对正常水质与异常水质进行分类。结果表明,卷积神经网络模型的水质识别率为99.38%,完全达到水质识别的要求。     

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN. In this paper, our team proposes a novel model (RBEBT) for the automatic classification of brain tumors. We use fine-tuned ResNet18 to extract the features of brain tumor images. The RBEBT is different from the traditional CNN models in that the randomized neural network (RNN) is selected as the classifier. Meanwhile, our team selects the bat algorithm (BA) to optimize the parameters of RNN. We use five-fold cross-validation to verify the superiority of the RBEBT. The accuracy (ACC), specificity (SPE), precision (PRE), sensitivity (SEN), and F1-score (F1) are 99.00%, 95.00%, 99.00%, 100.00%, and 100.00%. The classification performance of the RBEBT is greater than 95%, which can prove that the RBEBT is an effective model to classify brain tumors.     

A PC-based tissue classification system using artificial neuralnetworks

The author presents a pattern recognition algorithm and describes required instrumentation for ultrasound classification of simulated human-liver tissue abnormalities. The tissue is simulated by a liver phantom that mimics the tissue acoustically. The instrumentation used, a 50-MHz microcomputer-based data acquisition and analysis system designed by the author, digitizes the ultrasound backscattered signal from selected regions of the phantom and processes the digitized data for feature measurement. The algorithm is based on a three-layer backpropagation artificial neural network; trained to differentiate between simulated normal and abnormal tissue and to classify three types of simulated abnormalities. The results show that out of 28 cases the system classifies 25 correctly and fails to classify three cases. The reasons for this are discussed along with recommendations to increase the accuracy of classification     

Computer aided brain tumor detection system using watershed segmentation techniques

Magnetic Resonance Imaging (MRI) is an advanced medical imaging technique that has proven to be an effective tool in the study of the human brain. In this article, the brain tumor is detected using the following stages: enhancement stage, anisotropic filtering, feature extraction, and classification. Histogram equalization is used in enhancement stage, gray level co‐occurrence matrix and wavelets are used as features and these extracted features are trained and classified using Support Vector Machine (SVM) classifier. The tumor region is detected using morphological operations. The performance of the proposed algorithm is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The proposed system achieved 0.95% of sensitivity rate, 0.96% of specificity rate, 0.94% of accuracy rate, 0.78% of PPV, and 0.87% of NPV, respectively. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 297–301, 2015     

Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis. Their performance may sometime degrade due to missing information, selection of poor classification model and unavailability of quality data that are used to train the models for better prediction. This research work is an attempt to epileptic seizures detection by using a multi focus dataset based on EEG signals and brain MRI. The key steps of this work are: feature extraction having two different streams i.e., EEG using wavelet transformation along with SVD-Entropy, and MRI using convolutional neural network (CNN), after extracting features from both streams, feature fusion is applied to generate feature vector used by support vector machine (SVM) to diagnose the epileptic seizures. From the experimental evaluation and results comparison with the current state-of-the-art techniques, it has been concluded that the performance of the proposed scheme is better than the existing models.

     

基于BP和RBF神经网络对静电纺丝工艺参数的优化研究

针对静电纺丝在制备过程中易受到如聚合物含量、电压、推进速度和接收距离等工艺参数影响的问题,提出一种静电纺丝工艺参数的优化方法,以提升纳米纤维制备效率。以聚乳酸纳米纤维膜为研究对象,采用纤维直径为性能评价指标,设计实验获得训练和测试样本,借助BP(Back Propagation)和RBF(Radial Basis Function)神经网络构建不同工艺参数下的预测模型。结果表明：BP和RBF神经网络模型均能较好的对纤维直径进行预测,但RBF神经网络模型预测精度更高,其平均绝对误差(MAE)为12.125 nm,相对误差不超过7%。RBF神经网络建立的预测模型具有更高的稳定性,模型泛化能力更好,综合预测性能更加优越。所建立的模型可以帮助研究人员制备具有确定纤维直径的静电纺丝纳米纤维膜,实现对工艺参数的优化。     

Comparative analysis of Alzheimer's disease classification by CDR level using CNN,feature selection,and machine-learning techniques

Magnetic resonance imaging (MRI) of brain needs an impeccable analysis to investigate all its structure and pattern. This analysis may be a sharp visual analysis by an experienced medical professional or by a computer aided diagnosis system that can help to predict, what may be the recent condition. Similarly, on the basis of various information and technique, a system can be designed to detect whether a patient is prone to Alzheimer's disease or not. And this task of detection of abnormalities at an initial stage from brain MRI is a major challenge in the field of neurosciences. The main idea behind our research is to utilize the deep layers feature extraction benefited from deep neural network architecture, without extensive hardware resource training, and classifying the image on a basis of simple machine-learning algorithm with selected best features in order to reduce work load, classification error and hardware utilization time. We have utilized convolution neural network (CNN) layer using similar architecture like that of Alexnet with some parametric change, for the automatic extraction of features of images obtained from slice extraction of whole brain MRI whereas 13 manual features based on gray level co-occurrence matrix were also extracted to test the impact of this features on ranking. If we had only classified using CNN network, the misclassification rate was much higher. So, feature selection is achieved with feature ranking algorithms like Mutinffs, ReliefF, Laplacian and UDFS and so on and also tested with different machine-learning techniques like Support Vector Machine, K-Nearest Neighbor and Subspace Ensemble under different testing condition. The performance of the result is satisfactory with classification accuracy around 98% to 99% with 7:3 ratio of random holdout partition of training to testing image sets and also with fivefolds of cross-validation on the same set using a standardized template.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

基于SIFT变换和RBF神经网络的彩色全景图拼接算法

目的 为了复原存在平移、色彩差异、旋转、形变等问题的全景图,提出一种结合SIFT（尺度不变特征变换）和RBF神经网络的彩色全景图拼接算法。方法 通过SIFT算法匹配出两子图中对应的特征点,利用仿射变换解决图像间的旋转和形变问题,采用RBF神经网络纠正子图的色彩差异,最后利用权值矩阵融合技术实现重叠区域的融合。结果 文中算法在拼接效果上优于其他算法,其拼接效果DoEM值为0.902,图像重叠区域过度平滑,有效地避免了融合区域的亮度块或亮度线。结论 该算法效果好,可解决全景图复原过程中多方面的难题。     

A novel RBF neural network for radar emitter recognition based on Rough Sets

With the spreading of radar emitter technology, it is more difficult for traditional methods to recognize radar emitter signals. In this article, a new method is proposed to establish a novel radial basis function (RBF) neural network for radar emitter recognition based on Rough Sets theory. First of all, radar emitter signals describing words are processed by Rough Sets, and the importance weight of each attribute is obtained and the classification rules are extracted. The classification rules are the basis of initial centers of Rough k-means. These initial centers can reduce the computational complexity of Rough k-means efficiently because of a priori knowledge from Rough Sets. In addition, basis functions of neural units of an RBF neural network are improved with attribute importance weights based on Rough Sets theory. The novel network structure makes the RBF neural network more effective. The simulation results show that novel RBF neural network radar emitter recognition can recognize radar emitter signals more effectively than a traditional RBF neural network, because of the improved Rough k-means and the network structure with attribute importance weights.     

基于缸盖振动信号频域特征识别气缸压力的研究

在不同的燃烧状况下同时测量缸盖表面振动信号和缸内压力信号,对平均处理后的信号进行频域分析,发现缸内压力信号中相对于基频前50阶的谐波分量包含了所关心的主要信息.根据频域分析得到的复数谱的对称特性建立了训练样本,并对建立的BP和RBF神经网络进行训练.训练的结果表明RBF神经网络可以在更短的训练时间内,获得更小的均方误差.利用不同的神经网络进行了缸内压力信号的识别,识别的结果表明,RBF神经网络识别的精度高于BP神经网络.     

光纤陀螺刻度因子的建模方法

针对低精度光纤陀螺(FOG)刻度因子线性度较差的问题,提出了采用径向基函数(RBF)神经网络对刻度因子进行建模的方法,以减小光纤陀螺输出误差。通过测量数据对 RBF 神经网络进行训练,获得神经网络参数,根据神经网络结构和参数可以得到非线性刻度因子的解析表达式,将其作为刻度因子的模型,来提高 FOG 的精度。同时将 RBF 神经网络对刻度因子进行建模的结果与传统的建模结果进行了比较,验证了采用 RBF 神经网络对低精度刻度因子建模是非常有效的。     

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.