New developments in the diagnostic procedures to reduce prospective biopsies breast

Ayed, Norhene Gargouri Ben; Masmoudi, Alima Dammak; Sellami, Dorra; Abid, Riadh

doi:10.1109/icabme.2015.7323288

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Texture analysis plays an important role in mammographic image analysis fields ( [20] [21] [22] [23]). The proposed technique of texture Monogenic-GLLD provides a new 3-D feature vector based on joining the Gray Level and Local Difference (GLLD), the LP, and the LS information.…”

Section: The Proposed Technique Monogenic Gray Level and Local Difference-based Texture Feature Extractionmentioning

confidence: 99%

“…If the curvature is elliptical, the sign is positive, otherwise, when the curve is hyperbolic, the sign is negative. Lin Zhang et al [27] investigated the problem of rotation invariant texture classification and proposed the Monogenic Local Binary Pattern, this latter is a new textural representation based on the combination of the local phase information, the LS type and the LBP riu2 [26]. The main limitation of using the LBP code is that it may give the same results with two completely different gray levels when the differences with the neighbors are the same.…”

Section: The Proposed Technique Monogenic Gray Level and Local Difference-based Texture Feature Extractionmentioning

confidence: 99%

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Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

Gargouri

Mokni

Damak³

et al. 2021

Preprint

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Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the lesions based on mammographic images is playing an essential role to assist experts. A novel Computer-Aided Diagnosis (CADx) scheme of breast lesion classification is proposed in this paper based on an optimized combination of texture and shape features using machine and deep learning algorithms for mass classification as benign-malignant namely C(M-ZMs)*. The main advantage of using Zernike moments for shape feature extraction is their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our case. We implemented for texture feature extraction the Monogenic-Local Binary Pattern taking the advantage of lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. Therefore, we used Zernike moments for shape feature extraction due to their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our proposed system. The proposed system proves its performance on some challenging breast cancer cases where the lesions exist in dense breast tissues. Validation has been undertaken on 520 mammograms from the Digital Database for Screening Mammography Database (DDSM), yielding an accuracy rate of 99.5\%.

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Section: The Proposed Technique Monogenic Gray Level and Local Difference-based Texture Feature Extractionmentioning

confidence: 99%

Section: The Proposed Technique Monogenic Gray Level and Local Difference-based Texture Feature Extractionmentioning

confidence: 99%

Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

Gargouri

Mokni

Damak³

et al. 2021

Preprint

Self Cite

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A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography

Hernández-López

Gómez-Flores

2022

XXVII Brazilian Congress on Biomedical Engineering

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Artificial Intelligence in Breast Cancer Diagnosis: A Review

Karampotsis,

Panourgias,

Dounias

2024

Learning and Analytics in Intelligent Systems

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New developments in the diagnostic procedures to reduce prospective biopsies breast

Cited by 7 publications

References 5 publications

Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

A Classifier Ensemble Method for Breast Tumor Classification Based on the BI-RADS Lexicon for Masses in Mammography

Artificial Intelligence in Breast Cancer Diagnosis: A Review

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