Computer Aided Diagnosis System to Detect Breast Cancer Pathological Lesions

López, Yosvany; Novoa, Andra; Guevara, Miguel Ángel; Quintana, Nicolás; Silva, Augusto

doi:10.1007/978-3-540-85920-8_56

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…ANNs are seamless representation and classification models that learn simultaneously an appropriate hidden representation of the input data and a discriminative model that connects these hidden representations with the discriminant information. Recently, ANNs have grabbed attention in many areas, such as biomedicine [31], mainly for two reasons: the very good results obtained in real problems such as image recognition or natu-285 ral language processing; and the possibility to train them with very large data sets on clusters of graphical processing units (GPUs). In contrast to other classification problems, fraud detection entails a very difficult detection problem: first, it is an extremely unbalanced problem where the ratio between gen-290 uine and fraudulent transactions is greater than 5, 000 : 1; second, fraudulent transactions use to mimic genuine transactions in order to avoid being detected.…”

Section: Methodsmentioning

confidence: 99%

End-to-end neural network architecture for fraud scoring in card payments

Gmez

Arvalo

Paredes

et al. 2018

Pattern Recognition Letters

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • A completely new solution for fraud scoring in card payments validated by one of the biggest bank in the world • Experiments were performed with real dataset with more than 900 millions of credit card operations • Experiments performance is comparable to state of the art solutions but without any human intervention

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Section: Methodsmentioning

confidence: 99%

End-to-end neural network architecture for fraud scoring in card payments

Gmez

Arvalo

Paredes

et al. 2018

Pattern Recognition Letters

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“…Several types of extracted features (e.g. intensity statistics, shape and texture) from mammograms have been combined to form subsets of features, which extensively provided significant information for lesions classification [32] [33] [34] [35]. However, selecting the most appropriate subset of features is still a very difficult task; usually a satisfactory instead of the optimal feature subset is searched.…”

Section: B Feature Selection Methodsmentioning

confidence: 99%

Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection

Pérez¹,

Guevara²,

Silva³

et al. 2014

Annals of Computer Science and Information Systems

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This paper proposed a comprehensive algorithm for building machine learning classifiers for Breast Cancer diagnosis based on the suitable combination of feature selection methods that provide high performance over the Area Under receiver operating characteristic Curve (AUC). The new developed method allows both for exploring and ranking search spaces of imagebased features, and selecting subsets of optimal features for feeding Machine Learning Classifiers (MLCs). The method was evaluated using six mammography-based datasets (containing calcifications and masses lesions) with different configurations extracted from two public Breast Cancer databases. According to the Wilcoxon Statistical Test, the proposed method demonstrated to provide competitive Breast Cancer classification schemes reducing the number of employed features for each experimental dataset.

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“…Machine Learning Classifier (MLC) approaches have been reported in the past few years for mammography images analysis and classification with different degrees of success [6,7,19,[21][22][23][24][25][26][27][28][29][30] (see discussion in Section 3). In this work we consider SVM and ANN based MLC.…”

Section: Machine Learning Classifiers Explorationsmentioning

confidence: 99%

“…For research scientists, there are several interesting research topics in cancer detection and diagnosis systems, such as high-efficiency, high-accuracy lesion detection algorithms, including the detection of calcifications and masses, architectural distortions, bilateral asymmetries, etc. [6,7]. Radiologists, on the other hand, are paying more attention to the effectiveness of clinical applications of CAD methods.…”

Section: Introductionmentioning

confidence: 98%

Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis

et al. 2011

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This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.

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Computer Aided Diagnosis System to Detect Breast Cancer Pathological Lesions

Cited by 6 publications

References 9 publications

End-to-end neural network architecture for fraud scoring in card payments

End-to-end neural network architecture for fraud scoring in card payments

Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection

Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis

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