Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.     

Improved Rooftop Detection in Aerial Images with Machine Learning

In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing system that detects buildings in such images. We briefly review four algorithms that we selected to improve rooftop detection. The data sets were highly skewed and the cost of mistakes differed between the classes, so we used ROC analysis to evaluate the methods under varying error costs. We report three experiments designed to illuminate facets of applying machine learning to the image analysis task. One investigated learning with all available images to determine the best performing method. Another focused on within-image learning, in which we derived training and testing data from the same image. A final experiment addressed between-image learning, in which training and testing sets came from different images. Results suggest that useful generalization occurred when training and testing on data derived from images differing in location and in aspect. They demonstrate that under most conditions, naive Bayes exceeded the accuracy of other methods and a handcrafted classifier, the solution currently used in the building detection system.     

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features.     

Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images

The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a better identification of different animal species and individual animals. In this work, we propose an effective approach for automatic cattle recognition based on the multiple features of muzzle points and the cattle face images. The proposed method deals the cattle recognition problem as a classification problem among the multiple linear regression models and provides a new theory for the recognition of individual cattle. The group sparse signal representation based classification offers the key to addressing this problem using L2-minimization. In this paper, a comparative study among the well-established handcrafted texture feature extraction techniques and the appearance-based feature extraction techniques is also presented. A detailed set of experimental results on muzzle point image database is also carried to prove the theory. Our method has achieved 93.87% identification accuracy which demonstrates the superiority of the proposed method than the other existing machine learning based recognition algorithms.     

基于分类学习的去雾后图像质量评价算法

针对现有去雾后图像质量评价算法少、针对性弱和有效性差等问题, 本文提出一种基于分类学习的去雾后图像质量评价算法.该算法通过分析去雾后图像本身所蕴含的质量特征, 提取出基于图像增强、图像复原、统计先验以及人类视觉系统 (Human visual system, HVS) 的度量指标; 并在本文数据库基础上, 利用支持向量机 (Support vector machine, SVM) 将质量评价问题转换为分类问题.实验结果表明, 该算法与已有评价方法相比, 在获得高效分类评价结果的同时, 具有较好的实用性和主观一致性.     

基于改进多隐层极限学习机的电网虚假数据注入攻击检测

虚假数据注入攻击(False data injection attacks, FDIA)严重威胁了电力信息物理系统(Cyber-physical system,CPS)的状态估计,而目前大多数检测方法侧重于攻击存在性检测,无法获取准确的受攻击位置.故本文提出了一种基于灰狼优化(Gray wolf optimization, GWO)多隐层极限学习机(Multi layer extreme learning machine, ML-ELM)的电力信息物理系统虚假数据注入攻击检测方法.所提方法将攻击检测看作是一个多标签二分类问题,不仅将用于特征提取与分类训练的极限学习机由单隐层变为多隐层,以解决极限学习机特征表达能力有限的问题,且融入了具有强全局搜索能力的灰狼优化算法以提高多隐层极限学习机分类精度和泛化性能.进而自动识别系统各个节点状态量的异常,获取受攻击的精确位置.通过在不同场景下对IEEE-14和57节点测试系统上进行大量实验,验证了所提方法的有效性,且分别与极限学习机、未融入灰狼优化的多隐层极限学习机以及支持向量机(Support vector machine, SVM)相比,所提方...     

Wind power ramp event detection with a hybrid neuro-evolutionary approach

In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning machine (ELM), with evolutionary algorithms to optimize the trained models and carry out a feature selection on the input variables. The phenomenon under study occurs with a low probability, and for this reason the classification problem is quite unbalanced. Therefore, is necessary to resort to techniques focused on providing a balance in the classes, such as the synthetic minority over-sampling technique approach, the model applied in this work. The final model obtained is evaluated by a test set using both ELM and support vector machine algorithms, and its accuracy performance is analyzed. The proposed approach has been tested in a real problem of WPREs detection in three wind farms located in different areas of Spain, in order to see the spatial generalization of the method.

     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

Fast-gesture recognition and classification using Kinect: an application for a virtual reality drumkit

In this paper, we present a system for the detection of fast gestural motion by using a linear predictor of hand movements. We also use the proposed detection scheme for the implementation of a virtual drumkit simulator. A database of drum-hitting motions is gathered and two different sets of features are proposed to discriminate different drum-hitting gestures. The two feature sets are related to observations of different nature: the trajectory of the hand and the pose of the arm. These two sets are used to train classifier models using a variety of machine learning techniques in order to analyse which features and machine learning techniques are more suitable for our classification task. Finally, the system has been validated by means of the Kinect application implemented and the participation of 12 different subjects for the experimental performance evaluation. Results showed a successful discrimination rate higher than 95 % for six different gestures per hand and good user experience.     

Induction machine fault detection using clone selection programming

A clonal selection programming (CSP)-based fault detection system is developed for performing induction machine fault detection and analysis. Four feature vectors are extracted from power spectra of machine vibration signals. The extracted features are inputs of an CSP-based classifier for fault identification and classification. In this paper, the proposed CSP-based machine fault diagnostic system has been intensively tested with unbalanced electrical faults and mechanical faults operating at different rotating speeds. The proposed system is not only able to detect electrical and mechanical faults correctly, but the rules generated is also very simple and compact and is easy for people to understand, This will be proved to be extremely useful for practical industrial applications.     

一种基于学习的自动图像配准检验方法

图像配准是众多具体应用的共性核心技术, 如图像融合, 变化检测等. 然而, 当参考图像经过变换后, 如何自动地确定变换后的图像是否与目标图像真正达到了配准仍然是目前文献中一个尚未很好解决的问题. 究其原因, 主要是很难找到一种图像相似性的度量方法来有效地对配准后的图像进行评价. 不同于传统的方法, 本文提出了一种基于学习的相似性度量方法, 即将图像配准的度量问题转化为模式分类问题, 由基于机器学习设计的分类器自动检验图像是否配准. 本文对 400 组图像进行了配准检验, 实验结果显示了该方法的可行性和可靠性. 尽管本文方法的具体实现是针对基于 Fourier-Mellin 变换的配准算法, 但这种基于学习的图像配准检验思想同样可以应用到其他配准方法中.     

自适应残差加权的多元素协同表示分类算法

提出了一种残差加权的多元素协同表示算法. 该算法针对SRC的单一鉴别性不足,对样本提出样本与字典的多元素分解并分别进行相应的协同表示,自适应地学习出多元素的残差权重并进行线性加权,从而提高分类的性能. 实验表明：自适应残差加权的多元素协同表示分类算法,能够有效提高识别性能.     

一种改善的基于支持向量机的边缘检测算子

支持向量机是一种新的机器学习方法。它建立在统计学习理论基础上,较好地解决了小样本的学习问题。由于其出色的学习性能,该技术已经成为当前国际机器学习界的研究热点。文中提出了一种基于支持向量机的图像边缘检测新方法。这种方法介绍了如何使用支持向量机来高效的检测图像的边缘。首先用几个边缘简单的图像对支持向量机进行训练,然后使用支持向量分类方法进行边缘检测。针对实际图像的边缘检测实验表明,支持向量机可以有效地进行图像的边缘检测,其检测效果和传统的Canny边缘检测算子相当。     

Detection of cardiac amyloidosis on electrocardiogram images using machine learning and deep learning techniques

Cardiac amyloidosis is an uncommon disease that has been known for a long time. Moreover, modern advancement in noninvasive imaging of heart via ultrasound, magnetic resonance imaging has enhanced the detection of secret cardiac amyloidosis in patients identified with the heart disease. This article focused on detecting the heart disease especially cardiac amyloidosis on electro cardio gram images using recent technology of both machine learning and deep learning approaches. In addition, apart from detecting the disease on images, we are categorizing the heart images as normal and cardiac amyloidosis if any deviations occur. For CA disease identification along with its classification, 300 cardiac images have taken and those images are analyzed using machine learning algorithms namely nearest centroid, gradient boosting and random forest. Several metrics such as precision, recall, f-score, sensitivity, accuracy, and confusion matrix based on binary classification which classifies the images into positive (CA) and negative (non-CA) are estimated. Among these approaches, gradient boosting method achieves 95% accuracy as better outcomes which measure the model performance in detecting cardiac amyloidosis disease as well as ECG images are categorized into either normal or abnormal via classification metrics. Furthermore, we applied deep learning based neural network “DeepNet” model is applied on augmented data along with CNN which attains 93% accuracy in CA disease identification.     

Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions

In this paper we propose a machine learning approach to classify melanocytic lesions as malignant or benign, using dermoscopic images. The lesion features used in the classification framework are inspired on border, texture, color and structures used in popular dermoscopy algorithms performed by clinicians by visual inspection. The main weakness of dermoscopy algorithms is the selection of a set of weights and thresholds, that appear not to be robust or independent of population. The use of machine learning techniques allows to overcome this issue. The proposed method is designed and tested on an image database composed of 655 images of melanocytic lesions: 544 benign lesions and 111 malignant melanoma. After an image pre-processing stage that includes hair removal filtering, each image is automatically segmented using well known image segmentation algorithms. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters. The detection of particular dermoscopic patterns associated with melanoma is also addressed, and its inclusion in the classification framework is discussed. The learning and classification stage is performed using AdaBoost with C4.5 decision trees. For the automatically segmented database, classification delivered a specificity of 77% for a sensitivity of 90%. The same classification procedure applied to images manually segmented by an experienced dermatologist yielded a specificity of 85% for a sensitivity of 90%.     

Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model, a parallel version of SVM training is implemented. On ten stacks of fluorescence images labeled by experts, each of which owns one hundred 512 ·512 images with in total 4906 ROIs and 72056 labeled events, event detection with proposed method takes 19 seconds, while human labeling roughly costs 60 hours. With human labeling as the standard, the accuracy of our method achieves an F-value of about 0.81. This method is much faster than human detection and expects to be more precise with bigger data. It also can be expanded to a series of event detection with similar properties and improve efficiency of detection greatly.     

Spectrum-image dual-modality fusion empowered accurate and efficient classification system for traditional Chinese medicine

Traditional Chinese medicine (TCM) influences the Chinese and global medical systems, with its quality essential to its effectiveness. The origin of TCM material impacts the quality of the same TCM materials. However, the existing origin classification methods of the same TCM materials from different places mainly have two disadvantages: slow processing speed and extensive experience. To address these issues, a fast and real-time technology, laser-induced breakdown spectroscopy (LIBS), is introduced into our solution. We propose a TCM classification system that combines one-dimensional LIBS spectra with two-dimensional images. This dual-modality fusion approach represents a significant advancement in multi-view data analysis for TCM classification. As a case study, we focus on wolfberry and construct a new dataset comprising 10,800 pairs of LIBS spectrum and image data to fill the gap. To achieve superior multiple feature fusion, a two-stage fusion network (TFNet) in a coarse-to-fine way is proposed. In the first coarse fusion, the Depth Attention Fusion (DAF) module is applied to extract the key features of stacked spectrum and image. In the second fine fusion, the Line to Area (LTA) module entirely focuses on and highlights the critical spectral line features. Experimental accuracy is over 0.99 with less computation and parameters, indicating the high efficiency and accuracy of the proposed TFNet. Therefore, the classification system achieves exceptional accuracy and efficiency due to its simple sample preparation, real-time data collection and the high-accuracy lightweight network.     

A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault

A novel methodology for early diagnosis of rolling element bearing fault is employed based on continuous wavelet transform (CWT) and support vector machine (SVM). CWT is especially suited for analyzing non-stationary signals in time–frequency domain where time information is retained as well as frequency content. To better approximate non-stationary vibration signals from rolling element bearing, a wavelet choice criterion is established to select an appropriate mother wavelet for feature extraction. The Shannon wavelet is picked out of several considered wavelets. The classification tree kernels (CTK) are constructed to address nonlinear classification of the characteristic samples derived from the wavelet coefficients. By using Fuzzy pruning strategy, a large variety of classification trees are generated. The trees with diverse structures can effectively explore intrinsic information among samples. Then, the tree kernel matrices can be acquired through ensemble statistical learning, which eventually reveal the similarity of samples objectively and stably. Under such architecture of kernel methods, a classification tree kernel based support vector machine (CTKSVM) is proposed to identify bearing fault. The performance of the methodology involving CWT and CTKSVM (CWT–CTKSVM) is evaluated by cross validation and independent test. The results show that the CWT–CTKSVM totally is superior to other SVM methods with common kernels. Therefore, it is a prospective technique for detection and identification of rolling element bearing fault.