Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis

El-Soud, Mohamed W. Abo; Zyout, Imad; Hosny, Khalid M.; Eltoukhy, Mohamed Meselhy

doi:10.1109/access.2020.3008038

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…Chaos theory and image moments are mathematical powerful tools for security and characterizing image features. They have been used widely in many applications such as image encryption [1][2][3][4], image watermarking [5,6], face recognition [7,8] , and medical image analysis [9,10]. Chaos theory describe the behavior of dynamical system (chaotic system) having rapid decay of the correlations and highly sensitive to initial conditions and system parameters.…”

Section: Introductionmentioning

confidence: 99%

Image encryption via a 2-D Logistic-Baker map and fractional discrete Meixner moments

Karawia

Fouda

2022

Preprint

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In this paper, we introduce an efficient and robust image encryption anddecryption scheme based on 2-D chaotic map and 2-D fractional discreteorthogonal moments. In the confusion stage, the compound LogisticBaker map is used to enhance the complexity and generate pseudorandom sequences. In order to resist a variety of attacks and increasesecurity, Fractional Discrete Meixner Moments (FrDMMs) are used in thediffusion stage. FrDMMs had been computed using the eigenvalues andthe corresponding multiplicity of the discrete Meixner polynomials. Thesecret key is created by combining the Logistic-Baker map and FrDMMs.The experimental results and the security analysis demonstrate that theproposed algorithm is robust against existing security attacks and hasa large key space, high key sensitivity, and a strong encryption effect.

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Section: Introductionmentioning

confidence: 99%

Image encryption via a 2-D Logistic-Baker map and fractional discrete Meixner moments

Karawia

Fouda

2022

Preprint

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“…Eltoukhy et al [13] used a set of features obtained using exact Gaussian-Hermite moments and employed three different classifiers. The successful employment of orthogonal moments encourage the authors to integrate three kinds of orthogonal moments in [14].…”

Section: Introductionmentioning

confidence: 99%

A Grey Wolf-Based Method for Mammographic Mass Classification

et al. 2020

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Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871.

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Credit Risk Prediction Model of Digital Rural Finance Based on PSO-SVM

Huang

2023

Lecture Notes on Data Engineering and Communications Technologies

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Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis

Cited by 9 publications

References 36 publications

Image encryption via a 2-D Logistic-Baker map and fractional discrete Meixner moments

Image encryption via a 2-D Logistic-Baker map and fractional discrete Meixner moments

A Grey Wolf-Based Method for Mammographic Mass Classification

Credit Risk Prediction Model of Digital Rural Finance Based on PSO-SVM

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