An improved breast tissue density classification framework using bag of features model

Chougrad, Hiba; Zouaki, Hamid; Alheyane, Omar

doi:10.1109/cist.2016.7805081

Cited by 6 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A newly proposed CAD system (Hiba et al, 2016) aims to processed/enhanced then classified the digitized mammograms automatically into one of four categories in the density scale BI-RADS. The developed framework assisted radiologists by providing an automatic system for detecting and diagnosing possible cancers in mammograms.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in (Hiba et al, 2016) were the first to propose the technique for CLBP as a local feature extractor to generalize and complete LBP. They used the Outex and CUReT databases.…”

Section: Related Workmentioning

confidence: 99%

“…From another perspective, there are demands for developing Computer Aided Diagnosis (CAD) systems in order to engage it as a second reader for classifying the abnormalities in the mammogram. Also, it has been proven in many studies the high accuracy results and the significant performance that presented by CAD system (Hiba et al, 2016;Li et al, 2004;Mustra et al, 2010;Vaidehi and Subashini, 2015a). Additionally, CAD system can be used to recognize the degree of density in the breast profile (Vaidehi and Subashini, 2015a).…”

Section: Introductionmentioning

confidence: 98%

“…Secondly, the system segments the mammogram region of interest and extracts features using HOG and CLBP-HF techniques and forwarding the data into the k-Nearest Neighbor classifier to output a result showing the breast is either fatty, grandeur or dense. The evaluation revealed that the proposed performs the best out of the (Bosch et al, 2006;Hiba et al, 2016;Mustra et al, 2010;Silva and Menotti, 2012;Vaidehi and Subashini, 2015a) created a benchmark in the literature and yields an accuracy of 96.4, 96.59% recall and 96.75% precision outperforming the benchmark. This study presented a robust system for measure the breast density with high accuracy and significant performance.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Approach of Texture Feature and Gradient Orientation for Computer Aided Diagnosis System Based on Breast Density Classification

Alabdulali¹,

Dris²,

Alqahtani³

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

A Computer-Aided Diagnosis (CAD) system can perform an accurate diagnosis and help radiologists by presenting a second opinion about breast density. However, the development of a robust CAD system for breast density classification is still an open problem. In this study, we proposed a CAD system based on hybrid intelligent machine learning technique for automatic classification of breast density on mammogram images. The proposed technique employs gradient orientation pattern HOG and texture descriptor CLBP-HF as features and K Nearest Neighbor (KNN) as classifier. The experiments were carried out on benchmarks public domain MIAS and DDSM datasets. The classification accuracy is 96.4% whereas recall and precision are 96.59 and 96.75% on MIAS dataset. Moreover, the comparison with the state-of-the-art breast density classification methods shows that the proposed method outperforms the existing methods on both MIAS and DDSM datasets, the improvement is significant on both datasets. The proposed method will help radiologists in assessing the breast density, which is important for breast cancer diagnosis.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Authors in (Hiba et al, 2016) were the first to propose the technique for CLBP as a local feature extractor to generalize and complete LBP. They used the Outex and CUReT databases.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Approach of Texture Feature and Gradient Orientation for Computer Aided Diagnosis System Based on Breast Density Classification

Alabdulali¹,

Dris²,

Alqahtani³

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Their experiment achieved 100.00% classification Accuracy for ductal carcinoma in situ, 98.88% classification Accuracy for invasive carcinoma, and 100.00% classification Accuracy for normal image classification. A mammogram (DDSM) image database has been classified by Hiba et al [ 179 ] by SVM along with the Bag of Feature method. Firstly the authors extract LBP and quantize the binary pattern information for feature extraction.…”

Section: Performance Of Different Classifier Model On Breast Imagementioning

confidence: 99%

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Nahid

Kong

2017

Computational and Mathematical Methods in Medicine

106

View full text Add to dashboard Cite

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

show abstract

Artificial Intelligence in Breast Cancer Diagnosis: A Review

Karampotsis,

Panourgias,

Dounias

2024

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

An improved breast tissue density classification framework using bag of features model

Cited by 6 publications

References 14 publications

A Hybrid Approach of Texture Feature and Gradient Orientation for Computer Aided Diagnosis System Based on Breast Density Classification

A Hybrid Approach of Texture Feature and Gradient Orientation for Computer Aided Diagnosis System Based on Breast Density Classification

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Artificial Intelligence in Breast Cancer Diagnosis: A Review

Contact Info

Product

Resources

About