Diagnosis of masses in mammographic images based on Zernike moments and local binary attributes

Laroussi, Malek Gargouri; Ayed, Norhene Gargouri Ben; Masmoudi, Alima Damak; Masmoudi, Dorra Sellami

doi:10.1109/wccit.2013.6618683

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Finally, these features are fed to an SVM classifier. Kumar et al [39] Proposed two CAD systems for the classification of mammograms breast density for two and four "BI-RADS" classes consisting of features computed using different Law filters of varying lengths. The feature vectors are then fed to classifiers 'PNN,' 'NFC,' and 'SVM' to classify tissue density.…”

Section: B Computer-aided Diagnosis (Cad)mentioning

confidence: 99%

An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

et al. 2022

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Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC's early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods.

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Section: B Computer-aided Diagnosis (Cad)mentioning

confidence: 99%

An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

et al. 2022

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“…Muramatsu et al [22] has analysed radial local ternary patterns and obtained an A z value of 0.90 with 376 ROIs. Laroussi et al achieved an A z value of 0.96 for 160 ROIs using Zernike moments and LBP [23]. In [24], central and Hu moments along with Haralick's features were analysed with 600 training images and obtained an A cc of 89.90% for malignant cases and 87.40% for benign massses with 200 test images.…”

Section: Introductionmentioning

confidence: 99%

Analysis of 2D singularities for mammographic mass classification

Rabidas

Chakraborty

Midya

2016

IET Computer Vision

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Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet‐II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet‐II coefficients and statistical co‐variates, derived from the Ripplet‐II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten‐fold cross‐validation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini‐MIAS and 200 images from the Digital Database for Screening Mammography database, respectively.

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Edge Weighted Local Texture Features for the Categorization of Mammographic Masses

et al. 2017

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Diagnosis of masses in mammographic images based on Zernike moments and local binary attributes

Cited by 9 publications

References 11 publications

An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

Analysis of 2D singularities for mammographic mass classification

Edge Weighted Local Texture Features for the Categorization of Mammographic Masses

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