Retinal Blood Vessel Segmentation Using Extreme Learning Machine

2017 ◽  
Vol 21 (7) ◽  
pp. 1280-1290
Author(s):  
Fan Guo ◽  
◽  
Da Xiang ◽  
Beiji Zou ◽  
Chengzhang Zhu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Gabor Filter ◽  
Hessian Matrix ◽  
Vessel Segmentation ◽  
Comparative Performance ◽  
Multi Scale ◽  
Learning Technique ◽  
Learning Machine ◽  
Vessel Extraction ◽  
Elm Classifier

Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

scholarly journals Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

2019 ◽  
Vol 27 (1(133)) ◽  
pp. 67-77 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Xu Gao ◽  
Zefei Zhu ◽  
Xudong Hu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Dictionary Learning ◽  
Gabor Filter ◽  
Differential Evolution Algorithm ◽  
Adaptive Mutation ◽  
Multi Scale ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Learning Machine

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

scholarly journals FPGA Implementation of Weighted Online Sequential Extreme Learning Machine for Data Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 6551-6557
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Bench Mark ◽  
Hardware Platform ◽  
Learning Speed ◽  
Field Programmable ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Elm Classifier ◽  
Short Event

To design an efficient embedded module field-programmable gate array (FPGA) plays significant role. FPGA, a high speed reconfigurable hardware platform has been used in various field of research to produce the throughput efficiently. A now-a-days artificial neural network (ANN) is the most prevalent classifier for many analytical applications. In this paper, weighted online sequential extreme learning machine (WOS-ELM) classifier is presented and implemented in hardware environment to classify the different real-world bench-mark datasets. The faster learning speed, remarkable classification accuracy, lesser hardware resources, and short-event detection time, aid the hardware implementation of WOS-ELM classifier to design an embedded module. Finally, the developed hardware architecture of the WOS-ELM classifier is implemented on a high speed reconfigurable Xilinx Virtex (ML506) FPGA board to demonstrate the feasibility, effectiveness, and robustness of WOS-ELM classifier to classify the data in real-time environment.

scholarly journals An introductory study to machine learning and its application to employee turnover prediction

2019 ◽  
Author(s):  
João Pedro Pazinato Cruz de Oliveira ◽  
Leonardo Tomazeli Duarte
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Test Data ◽  
Employee Turnover ◽  
Learning Machine ◽  
Elm Classifier

The objective of this paper is to study the problem of employee turnover prediction and to develop a classifier that uses employee's data to identify those who have a greater tendency to leave the company voluntarily. For such purpose, the data of 8724 employees from a real Brazilian beverage company was used to train an Extreme Learning Machine (ELM) classifier, assigning to each sample a weight inversely proportional to the size of the respective class. After the training, the classifier displayed an overall accuracy of 79% of the test data.

scholarly journals Gait Recognition Based on the Feature Extraction of Gabor Filter and Linear Discriminant Analysis and Improved Local Coupled Extreme Learning Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Hongli Guo ◽  
Bin Li ◽  
Youmei Zhang ◽  
Yu Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Gabor Filter ◽  
Gait Recognition ◽  
Effective Means ◽  
Linear Discriminant ◽  
Gait Features ◽  
Learning Machine

A gait energy image contains much gait information, which is one of the most effective means to recognize gait characteristics. The accuracy of gait recognition is greatly affected by covariates, such as the viewing angle, occlusion of clothing, and walking speed. Gait features differ somewhat by angles. Therefore, how to improve the recognition accuracy of a cross-view gait is a challenging task. This study proposes a new gait recognition algorithm structure. A Gabor filter is used to extract gait features from gait energy images, since it can extract features of different directions and scales. We use linear discriminant analysis (LDA) to tackle the problem that the feature dimension restricts the process. Finally, the improved local coupled extreme learning machine based on particle swarm optimization is used for the classification process of the extracted features of the gait. The proposed method and other current mainstream algorithms are compared in terms of the recognition accuracy based on the CASIA-A and CASIA-B datasets, and the simulation results show that the proposed algorithm has good performance and performs well at cross-view gait recognition.

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