Breast tumor detection in double views mammography based on extreme learning machine

Wang, Zhiqiong; Qu, Qixun; Yu, Ge; Kang, Yan

doi:10.1007/s00521-014-1764-0

Cited by 25 publications

(8 citation statements)

References 30 publications

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“…In 2017, You and Zhou [208] employed ELM in prediction of protein-protein interactions (PPIs) and claimed to obtain satisfying performance. In 2018, You and Zhou [208] designed and implemented experiments to compare different kinds of classifiers' [197], Wang and Qu [186], Hu and Yang [51], Wang and Li [185] performance in predicting protein secondary structure. According their results, extreme learning machine (ELM) had the fastest training speed, but support vector machine (SVM) was the most accurate.…”

Section: Chemistry Applicationmentioning

confidence: 99%

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A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

271

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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Section: Chemistry Applicationmentioning

confidence: 99%

“…The trained ELM can classify the masses into benign or malignant accurately. Wang and Qu[186] put forward a breast tumor detection method based on ELM and mammogram. They fused geometry and textural features from two views of mammogram and optimized the feature model with feature selection.…”

mentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

271

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“…In [48], the eigenvector model was established by mathematical methods, and the geometric and texture feature sets were combined for breast cancer diagnosis on digital mammography. On the basis of this feature model, [22] proposed a fused feature model that blends features of single views with comparative features of double views to simulate the process of doctor's film reading. In [24], the feature model and classifier are validated respectively in breast mass detection, and local fusion features with sub-region density are established.…”

Section: A Feature Extractionmentioning

confidence: 99%

“…Common algorithms include ReliefF Method [61], Sequential Forward Selection (SFS) [22], [23] and Genetic Algorithm Selection (GAS) [22]- [24], [34], [42], [47], [49], [62], [63]. In the research process of references [22] and [23], three popular feature selection algorithms, GAS, impact value selection and SFS, are compared and tested. From the results, GAS is the algorithm with the best obvious effect on ELM classifier performance optimization.…”

Section: B Feature Selectionmentioning

confidence: 99%

Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review

et al. 2020

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Computer-Aided Diagnosis (CAD) can improve the accuracy of diagnosis effectively, reduce the rate of misdiagnosis, and provide the support for the valid decision. In clinical applications, high requirements are often imposed on the execution speed and accuracy of CAD systems. The classifier is regarded as the core of the CAD system, that is, the performance of the classifier will have a decisive influence on the operating affection of the CAD system. Extreme Learning Machine (ELM) is a fast learning algorithm using Single Hidden Layer Feedforward Neural Network (SLFN) structure. With its advantages in training speed, generalization performance and accuracy, ELM has draw attention in many research fields, including the development of CAD system. The applications of ELM in CAD are reviewed in this research. First, the mathematical model of ELM and framework of CAD system are briefly introduced. Then, the application of ELM in CAD is reviewed in detail, including the feature modeling method combined with ELM in CAD and the specific application of ELM. Finally, we summarized the current research status of CAD systems based on ELM, and the future work is prospected.INDEX TERMS Computer-aided diagnosis, extreme learning machine, machine learning, review. I. INTRODUCTION

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“…To our knowledge, there have been a few techniques aimed to match corresponding positions between CC and MLO view images [6]- [19]. Such techniques have been mainly classified as geometry-based method [20]- [23], similarity measurement method [9], [24], [25], and classifier-based method [3], [4], [15], [26]- [28]. Geometry-based method can be further divided into plane-based and space-based method.…”

Section: Introductionmentioning

confidence: 99%

Matching Corresponding Regions of Interest on Cranio-Caudal and Medio-Lateral Oblique View Mammograms

et al. 2019

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Automatically matching corresponding regions of interest (ROIs) on two-view images is valuable in breast cancer diagnosis, benefiting of saving time and cutting the workload. We propose a method for matching the corresponding ROIs by integrating the geometric model and image similarity searching. The geometric model is implemented by restoring a free breast in the 3D space from two-view preprocessed breast contours. Then, the possible position of the ROI center on cranio-caudal (CC)/medio-lateral oblique (MLO) view image is represented by three feature points in the 3D space. As the view changes, these points can be mapped onto the MLO/CC view image. A matching strip is created later according to the confidence interval, within which the specific position of the ROI can be located by image similarity searching. The experiments were conducted on 273 pairs of mammograms with 400 calcifications and 284 pairs with 300 masses to verify the accuracy of the geometric model and similarity searching. The mean absolute error between the curves and the ROI centers was 3.36 ± 2.90 mm. For 95% detection sensitivity, the confidence interval was ±8.77 mm. For calcifications, the mean distance between the centers of the matched ROIs and the reference was 3.92 ± 4.61 mm. About 93.46% cases had overlap greater than 50%, and 92.46% cases had overlap greater than 75%. For masses, the mean distance was 6.15 ± 7.08 mm. About 88.46% cases had overlap greater than 50%, and 85.58% cases had overlap greater than 75%. INDEX TERMS Mammogram, geometric model, matching ROIs, similarity measure.

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Breast tumor detection in double views mammography based on extreme learning machine

Cited by 25 publications

References 30 publications

A review on extreme learning machine

A review on extreme learning machine

Computer-Aided Diagnosis Based on Extreme Learning Machine: A Review

Matching Corresponding Regions of Interest on Cranio-Caudal and Medio-Lateral Oblique View Mammograms

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