Fast opposite weight learning rules with application in breast cancer diagnosis

Saki, Fatemeh; Tahmasbi, Amir; Soltanian-Zadeh, Hamid; Shokouhi, Shahriar B.

doi:10.1016/j.compbiomed.2012.10.006

Cited by 68 publications

(27 citation statements)

References 23 publications

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“…In general, for large databases, the input instances are divided into three equal-sized categories called training, validation, and testing; each category contains approximately 33% of the total input instances. To find the best architecture for the classifier, the average of 5 runs is computed [36,37]. In this paper, our prepared dataset, which contains the data of 126 cases, is partitioned into three groups.…”

Section: Resultsmentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

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An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the obtained breast-chest wall boundary. Finally, the obtained breast-chest wall boundary is accurately refined using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

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Section: Resultsmentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

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“…Due to the complex morphology of mass lesions that often appear with ambiguous and jagged contours and the nature of their surrounding fibroglandular breast tissue, the segmentation of masses is much more difficult compared with other findings such as calcification or architectural distortion. Several segmentation methods have been proposed in the literature to extract the precise contour of mammographic lesions which are roughly categorized into automatic [11,31,32], semiautomatic [33,34], and manually performed by the radiologist [18,35]. However, previous studies [36] have shown that there is no "one-fit-all" segmentation technique and none of the automatic and manual methods can provide fully accurate results for diverse mass lesions in mammograms.…”

Section: Segmentationmentioning

confidence: 99%

A New Computer‐Aided Diagnosis System with Modified Genetic Feature Selection for BI‐RADS Classification of Breast Masses in Mammograms

Boumaraf

Liu

Ferkous

et al. 2020

BioMed Research International

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Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

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“…The performance of the dynamic algorithm has been compared to the BBP algorithm in [26], [27]. The speed up training is calculated using the formula [28]- [30]:…”

Section: Discussion On Performance Of the Dbbplr Algorithmmentioning

confidence: 99%

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

Al_Duais¹,

Mohamad²

2017

JTIT

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Abstract-The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.Keywords-artificial neural network (ANN), batch back propagation algorithm, dynamic training rate, speed up training, accuracy training.

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Fast opposite weight learning rules with application in breast cancer diagnosis

Cited by 68 publications

References 23 publications

Breast-region segmentation in MRI using chest region atlas and SVM

Breast-region segmentation in MRI using chest region atlas and SVM

A New Computer‐Aided Diagnosis System with Modified Genetic Feature Selection for BI‐RADS Classification of Breast Masses in Mammograms

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

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