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Automatic recognition of acute lymphoblastic leukemia using multi-svm classifier

CURRENT SCIENCE 2018年第8期115卷 1512-1518页

作者： Mirmohammadi, Pouria Rasooli, Amirhossien Ashtiyani, Meghdad Amin, Morteza Moradi Deevband, Mohammad Reza Shahid Beheshti Univ Med Sci Fac Med Dept Biomed Engn & Med Phys Tehran Iran Univ Tehran Med Sci Dept Med Phys & Biomed Engn Tehran Iran

Acute lymphoblastic leukemia (ALL) is the most popular form of white blood cells cancer in children. It is classified into three forms of L1, L2 and L3. Typically, it is identified through screening of blood smearsvia pathologists. Since this is laborious and tedious, automatic systems are desired for suitable detection;but the high similarity between morphology of ALL forms and that of normal, reactive and a typical lymphocytes, makes the automatic detection a challenging problem. This study tried to improve the accuracy of detection based on principle component analysis (PCA). After segmenting nuclei of cells, numerous features were extracted. The first six components of this feature space were used for the binary and multiclass support vector machine classifiers. An expert pathologist was used to appraise this method as a gold standard. A collation with similar work indicated that using PCA instead of using exclusively selected features enhanced the average sensitivity and specificity of classification up to 10%. The results demonstrate that this algorithm performs better than similar studies. Its permissible efficiency for identifying ALL and its sub-types as well as other lymphocyte forms makes it an associate diagnostic device for pathologists.

关键词： ALL fuzzy c-means method PCA analysis svm classifier

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A low-power asynchronous hardware implementation of a novel svm classifier, with an application in a speech recognition system

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MICROELECTRONICS JOURNAL 2020年 105卷 104907-104907页

作者： Batista, Gracieth C. Oliveira, Duarte L. Saotome, Osamu Silva, Washington L. S. Technol Inst Aeronaut 50 Vila Acacias Sao Jose Dos Campos SP Brazil Fed Inst Maranhao Av Getulio Vargas BR-65030005 Sao Luis Maranhao Brazil Technol Inst Aeronaut Dept Elect Devices & Syst Area Elect Engn & Comp Sao Jose Dos Campos Brazil

Machine Learning (ML) has been applied in so many areas in reason of its robustness, usability, and reliability, mainly in hardware implementation. One of its well-known algorithms is the Support Vector Machine (svm), the simplest to be applied in hardware because of its mathematical modeling. In this study, we propose the implementation in hardware of svm multi-class classifiers within the asynchronous paradigm (i.e., without clock signal application) in a 4-stage pipeline architecture. With the purpose to evaluate the proposed architecture behavior, we used a Field Programmable Gate Array (FPGA) device to prototype the circuit. The proposed asynchronous svm classifier is applied in a Speech Recognition system of 30 classes where a hybrid training algorithm was processed in the Matlab software, known as PSO-svm training algorithm. Therefore, the training phase was processed in software in reason of its computational load. For the svm classification phase, we propose, for the first time to the best of our knowledge, an asynchronous pipeline architecture of four stages with Multiply-Accumulator (MAC) unit application and three different control circuits described from Extended Burst-Mode (XBM) and State Transition Graph (STG) specifications, leading to energy-efficient design. In order to validate the svm recognition results in the speech recognition application, the tests are from 60 speeches and 20 speakers, so it is a diversified and reliable data set of tests. The main goal here was to design a machine learning hardware implementation in a low power application and, through that, to prove that the asynchronous paradigm reduced power. As a result, we obtained a reduced power consumption of 5.72 mW, a fast average response time which was 0.61 is and the most area-efficient circuit (1315 LUTs);the accuracy in recognition success rate was another preoccupation, and it was very successful, 98% of success. Besides, we present comparisons with an asynchronous version of

关键词： Low power design Asynchronous hardware implementation svm classifier FPGA Prototyping

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3D palmprint recognition using unsupervised convolutional deep learning network and svm classifier

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IET IMAGE PROCESSING 2019年第5期13卷 736-745页

作者： Chaa, Mourad Akhtar, Zahid Attia, Abdelouahab Univ Ouargla Ouargla Algeria Univ Quebec Montreal PQ Canada Mohamed El Bachir El Ibrahimi Univ Bordj Bou Arrerridj Algeria

Since past decade, efforts are afoot to design better hand-based automatic person recognition systems. Among the various hand-based biometric traits, palmprint as a biometric characteristic is now gaining increased attention from both the academic and industrial communities owing to its highly distinctive texture patterns, features richness, and stability. Here, the authors propose a new 3D palmprint recognition framework based on an unsupervised convolutional deep learning network named PCANet. Specifically, the proposed framework first reconstructs illumination-invariant 3D palmprint images using Single Scale Retinex (SSR) algorithm. Then, PCANet topology is employed to extract discriminative features from SSR images. Finally, a multi-class support vector machine (svm) classification scheme is utilised to determine the identity of the person. Extensive experimental analysis on publicly available 3D palmprint PolyU dataset, which is composed of 8000 range images from 200 individuals, shows that proposed method outperforms existing approaches and is also able to attain 99.98% rank-1 accuracy.

关键词： palmprint recognition learning (artificial intelligence) image texture support vector machines image classification feature extraction convolutional neural nets SSR images multiclass support vector machine classification scheme svm classifier hand-based automatic person recognition systems hand-based biometric traits biometric characteristic academic communities industrial communities features richness 3D palmprint recognition distinctive texture patterns single scale retinex algorithm PCANet unsupervised convolutional deep learning network illumination-invariant 3D palmprint images PCANet topology 3D palmprint PolyU dataset

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Facial decomposition for expression recognition using texture/shape descriptors and svm classifier

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2017年 58卷 300-312页

作者： Lekdioui, Khadija Messoussi, Rochdi Ruichek, Yassine Chaabi, Youness Touahni, Raja Univ Bourgogne Franche Comte UTBM Le2i FRE2005 CNRSArts & Metiers F-90010 Belfort France Univ Ibn Tofail Lab Syst Telecommun & Ingn Decis LASTID BP 133 Kenitra 14000 Morocco

Automatic facial expression analysis is a challenging topic in computer vision due to its complexity and its important role in many applications such as human computer and social interaction. This paper presents a Facial Expression Recognition (FER) method based on an automatic and more efficient facial decomposition into regions of interest (ROI). First, seven ROIs, representing more precisely facial components involved in expression of emotions (left eyebrow, right eyebrow, left eye, right eye, between eyebrows, nose and mouth), are extracted using the positions of some landmarks detected by IntraFace (IF). Then, each ROI is resized and partitioned into blocks which are characterized using several texture and shape descriptors and their combination. Finally, a multiclass svm classifier is used to classify the six basic facial expressions and the neutral state. In term of evaluation, the proposed automatic facial decomposition is compared with existing ones to show its effectiveness, using three public datasets. The experimental results showed the superiority of our facial decomposition against existing ones and reached recognition rates of 96.06%, 92.03% and 93.34% for the CK, FEED and KDEF datasets, respectively. Then, a comparison with state-of-the-art methods is carried out using CK + dataset. The comparison analysis demonstrated that our method outperformed or competed the results achieved by the compared methods. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Facial components Texture and shape descriptors Facial expression recognition svm classifier ROI extraction

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Lung nodule segmentation and recognition using svm classifier and active contour modeling: A complete intelligent system

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COMPUTERS IN BIOLOGY AND MEDICINE 2013年第4期43卷 287-300页

作者： Keshani, Mohsen Azimifar, Zohreh Tajeripour, Farshad Boostani, Reza Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn Shiraz Iran

In this paper, a novel method for lung nodule detection, segmentation and recognition using computed tomography (CT) images is presented. Our contribution consists of several steps. First, the lung area is segmented by active contour modeling followed by some masking techniques to transfer non-isolated nodules into isolated ones. Then, nodules are detected by the support vector machine (svm) classifier using efficient 2D stochastic and 3D anatomical features. Contours of detected nodules are then extracted by active contour modeling. In this step all solid and cavitary nodules are accurately segmented. Finally, lung tissues are classified into four classes: namely lung wall, parenchyma, bronchioles and nodules. This classification helps us to distinguish a nodule connected to the lung wall and/or bronchioles (attached nodule) from the one covered by parenchyma (solitary nodule). At the end, performance of our proposed method is examined and compared with other efficient methods through experiments using clinical CT images and two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. Solid, non-solid and cavitary nodules are detected with an overall detection rate of 89%;the number of false positive is 7.3/scan and the location of all detected nodules are recognized correctly. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Lung nodules Detection Segmentation Connectivity recognition Active contour svm classifier

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Automatic detection of arrhythmias from an ECG signal using an auto-encoder and svm classifier

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2022年第2期45卷 665-674页

作者： Ojha, Manoj Kumar Wadhwani, Sulochna Wadhwani, Arun Kumar Shukla, Anupam Madhav Inst Sci & Technol Gwalior Madhya Pradesh India Indian Inst Informat Technol Pune Maharashtra India

Millions of people around the world are affected by arrhythmias, which are abnormal activities of the functioning of the heart. Most arrhythmias are harmful to the heart and can suddenly become life-threatening. The electrocardiogram (ECG) is an important non-invasive tool in cardiology for the diagnosis of arrhythmias. This work proposes a computer-aided diagnosis (CAD) system to automatically classify different types of arrhythmias from ECG signals. First, the auto-encoder convolutional network (ACN) model is used, which is based on a one-dimensional convolutional neural network (1D-CNN) that automatically learns the best features from the raw ECG signals. After that, the support vector machine (svm) classifier is applied to the features learned by the ACN model to improve the detection of arrhythmic beats. This classifier detects four different types of arrhythmias, namely the left bundle branch block (LBBB), right bundle branch block (RBBB), paced beat (PB), and premature ventricular contractions (PVC), along with the normal sinus rhythms (NSR). Among these arrhythmias, PVC is particularly a dangerous type of heartbeat in ECG signals. The performance of the model is measured in terms of accuracy, sensitivity, and precision using a tenfold cross-validation strategy on the MIT-BIH arrhythmia database. The obtained overall accuracy of the svm classifier was 98.84%. The result of this model is portrayed as a better performance than in other literary works. Thus, this approach may also help in further clinical studies of cardiac cases.

关键词： ECG Arrhythmia Auto-encoder svm classifier

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ECG Signal Preprocessing and svm classifier-Based Abnormality Detection in Remote Healthcare Applications

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IEEE ACCESS 2018年 6卷 9767-9773页

作者： Venkatesan, C. Karthigaikumar, P. Paul, Anand Satheeskumaran, S. Kumar, R. Anna Univ Fac Informat & Commun Engn Madras 600025 Tamil Nadu India Karpagam Coll Engn Dept Elect & Commun Engn Coimbatore 641032 Tamil Nadu India Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Anurag Grp Inst Dept Elect & Commun Engn Telengana 500088 India Natl Inst Technol Nagaland Dept Elect & Instrumentat Engn Dimapur 797103 Nagaland India

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, svm classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of svm classifier is better than other machine learning-based classifiers.

关键词： Adaptive filter arrhythmic beat classification ECG preprocessing svm classifier machine learning

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Fuzzy quasi-linear svm classifier: Design and analysis

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FUZZY SETS AND SYSTEMS 2021年 413卷 42-63页

作者： Yang, Cheng Oh, Sung-Kwun Yang, Bo Pedrycz, Witold Fu, ZunWei Univ Suwon Dept Comp Engn San 2-2 Wau Ri Hwaseong Si 445743 Gyeonggi Do South Korea Linyi Univ Res Ctr Big Data & Artificial Intelligence Linyi 276005 Shandong Peoples R China Univ Suwon Sch Elect & Elect Engn Hwaseong Si 18323 Gyeonggi Do South Korea Linyi Univ Sch Informat Sci & Engn Linyi 276005 Shandong Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst Warsaw Poland Qufu Normal Univ Sch Math Sci Qufu 273100 Shandong Peoples R China

Multiple Support Vector Machine (svm) classifier based on ensemble learning approaches could be enhanced from the view point of accuracy, but the performance of these classifiers closely depends on the initial condition of the partitioning method used in the design. Furthermore, these classifiers are more easily affected by noise and outliers. In this study, a novel fuzzy quasi linear svm classifier realized with the aid of a composite kernel function and Fuzzy C-Means (FCM) clustering is proposed. The objective of this approach is to reduce the effect of noise and outliers and also handle the overfitting problem through the synergistic effect of the two methods: First, Fuzzy C-Means (FCM) is used to partition the training dataset into several subsets as a preprocessing phase of the proposed classifier. Second, the composite kernel based on multiple linear kernel expression is considered to avoid overfitting problem. In more detail, each training data is assigned to the corresponding membership degree. Some data which are potential noise or outliers are assigned with lower membership degrees and thus yield a small contribution to the composite kernel function. Then, the composite kernel function for multiple local svms is constructed according to the distribution of training data. The designed fuzzy quasi-linear svm classifier is tested with both artificial and UCI data sets. It is also applied for sorting the problem of black plastic wastes being handled in the practice in order to verify the effective as well as efficient classification improvement. Experimental results demonstrate that the proposed method shows the elevated classification performance when compared to performance produced by the methods studied previously. ? 2020 Elsevier B.V. All rights reserved.

关键词： svm classifier Composite kernel function Membership degrees Black plastic wastes sorting Particle Swarm Optimization (PSO)

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Research on leg posture recognition of sprinters based on svm classifier

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INTERNATIONAL JOURNAL OF BIOMETRICS 2022年第3-4期14卷 367-382页

作者： He, Yang Hunan City Univ Coll Phys Educ Yiyang 413000 Hunan Peoples R China

In order to overcome the problems of low recognition rate, high time consumption and high misclassification rate caused by the difficulty in obtaining the global motion pattern information of sprinters in traditional posture recognition methods, a leg pose recognition method based on svm classifier is proposed. Using multivariate statistical model to denoise the sprint image, the effective leg movement pattern information of sprinters is extracted. In the svm classifier, the samples are divided by decision function to realise the recognition of sprinters' leg posture. In order to verify the effectiveness of the method in this paper, a comparative experiment is designed. Experimental results show that the recognition rate of the proposed method is more than 90%, the time consumption of recognition process is always less than 0.5 s and the misclassification rate of leg pose features is always below 5%, which fully demonstrates the high recognition performance of the method.

关键词： support vector machine svm classifier sprint wavelet transform feature vector leg pose recognition feature extraction

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Intelligent handwritten recognition using hybrid CNN architectures based-svm classifier with dropout

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第6期34卷 3294-3300页

作者： Ali, Amani Ali Ahmed Mallaiah, Suresha Taiz Univ Dept Comp Sci Taizi Yemen Kuvempu Univ Dept MCA & Comp Sci Shimoga India

Text recognition in Arabic handwritten scripts is an active research field. These recognition systems face numerous challenges, including enormous open data-bases, infinite variation in people's handwriting, and freestyle. In this manuscript, Authors model deep learning architecture which can efficiently be utilized to recognizing Arabic handwritten scripts. This work explored a new model for both single font and multi-font type which concentrate on two common classifiers which are: Support Vector Machine (svm) along with Convolutional Neural Network (CNN). Furthermore, authors protected the proposed model against the issue of over-fitting because of the strong performance of dropout technique. Both classification and feature extraction are done automatically. In the light of the error backpropagation method analysis, authors also have been proposed an innovative depth neural network training rule for maximum interval minimum classification error. In the meantime, max-margin minimum classification error (M3CE) and cross entropy are analyzed and hybridized to obtain better outcomes. Authors tested the proposed model on AHDB, AHCD, HACDB, and IFN/ENIT databases. The proposed model performance is compared with the accuracies of text recognition gained from state-of-the-art Arabic text recognition. The proposed model delivers favorable results. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

关键词： Arabic handwritten recognition CNN Deep convolution neural network Deep learning Dropout Image classification M3CE Over fitting svm classifier

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