An Optimized Feature Selection Method For Breast Cancer Diagnosis in Digital Mammogram using Multiresolution Representation

Eltoukhy, Mohamed Meselhy; Faye, Ibrahima

doi:10.12785/amis/080629

Cited by 28 publications

(25 citation statements)

References 20 publications

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“…It can be seen that the proposed method obtains better diagnostic performance. Even compared to [7], it is also comparable. It can be noted that [7] reaches the higher accuracy, but we choose the point where the classification accuracy rate is higher and the number of features is fewer.…”

Section: Compared With State-of-the-art Methodsmentioning

confidence: 88%

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Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

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Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

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Section: Compared With State-of-the-art Methodsmentioning

confidence: 88%

“…Mammography as the best valid tool has been widely used in early breast cancer detection [3][4][5][6][7]. However, the growing mammograms especially the large number of normal cases increase the reading burden of radiologist; it may lead to missing the subtle abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Dong¹,

Wang²,

Cheng-hui³

et al. 2017

Journal of Sensors

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“…That idea is present in the moment invariant approaches used for feature extraction in image processing [8], for example. With the exception of anomaly detection such as fraud detection [9], cancer diagnosis [10] or pulmonary embolism diagnosis [11] which uses the distributions´ extreme values, most applications use the central tendency of the distribution as decision level feature.…”

Section: A Relational Data Mining (Rdm)mentioning

confidence: 99%

Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining

Pereira¹,

Adeodato

Neto

2017

Proceedings of the 14th CONTECSI International Conference on Information Systems and Technology Management

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Abstract-This paper presents an approach for transforming data granularity in hierarchical databases for binary decision problems by applying regression to categorical attributes at the lower grain levels. Attributes from a lower hierarchy entity in the relational database have their information content optimized through regression on the categories´ histogram trained on a small exclusive labelled sample, instead of the usual mode category of the distribution. The paper validates the approach on a binary decision task for assessing the quality of secondary schools focusing on how logistic regression transforms the students´ and teachers´ attributes into school attributes. Experiments were carried out on Brazilian schools´ public datasets via 10-fold crossvalidation comparison of the ranking score produced also by logistic regression. The proposed approach achieved higher performance than the usual distribution mode transformation and equal to the expert weighing approach measured by the maximum Kolmogorov-Smirnov distance and the area under the ROC curve at 0.01 significance level.

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“…Wavelet transform is a sparse and efficient way to represent an image by improving the image quality multiresolution representation. The Wavelet transform is similar to filters that use a high pass filter and a low pass filter to decompose an image into sub frequency bands along the image's rows and columns (57,60) .…”

Section: Initial Data Preprocessingmentioning

confidence: 99%

“…For a given image, the run length of the line P (i, j) is defined as a number of lines with pixels of grey-level value i and line length value j (57,59,63) Being n as the total number of lines in the image, M as the number of grey levels and N the maximum line length, the authors (76) and (77) defined the following characteristics (Table 4.2). …”

Section:  Homogeneitymentioning

confidence: 99%

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

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More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method.In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis.In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings.Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix.For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests).The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS ® scale.The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications.The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques. VIII IX ResumoCada vez mais assistimos a um aumento global do número de métodos de apoio a decisão e diagnóstico assistido por computador, aplicados a diversas áreas da medicina.Na área de investigação do cancro da mama muitos são os trabalhos que têm sido desenvolvidos como segunda leitura de modo a reduzir o número de falsos positivos no diagnóstico.Neste estudo é apresentado um conjunto de técnicas de data mining que poderão ser aplicadas a um sistema de apoio à decisão na área do diagnóstico de cancro da mama.Esta abordagem tem por objetivo ajudar os clínicos na identificação de achados mamográficos como microcalcificações, massas e mesmo tecidos normais, de forma a evitar diagnósticos errados.Para isso, neste trabalho é usada uma base de dados fidedigna, de 410 imagens correspondentes a 115 pacientes, contendo análises prévias, realizadas por radiologistas, de microcalcificações, massas e tecidos considerados normais.Ao longo deste trabalho são utilizadas duas técnicas de extração de características, a matriz de coocorrência de níveis de cinza e a matriz de comprimento da linha de níveis de cinza.

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An Optimized Feature Selection Method For Breast Cancer Diagnosis in Digital Mammogram using Multiresolution Representation

Cited by 28 publications

References 20 publications

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

Optimal Categorical Attribute Transformation for Granularity Change in Relational Databases for Binary Decision Problems in Educational Data Mining

Using Data Mining Techniques to Support Breast Cancer Diagnosis

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