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Optimized generative adversarial network based breast cancer diagnosis with wavelet and texture features

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Abstract

Breast cancer is defined as a deadly disease, and it is the major cause of the increased mortality rate of women. Mammography is the main method for breast cancer diagnosis. Even in this era, the early diagnosis of breast cancer using mammogram images is a complex task. Deep learning approaches have demonstrated good applicability for diverse databases. In this line, this paper intends to propose a deep learning-based breast cancer detection model with the inclusion of steps like (i) pre-processing (ii) segmentation (iii) feature extraction (iv) classification. The preprocessing is carried out via CLAHE and the median filtering model. Subsequently, the pre-processed images are segmented using Fuzzy C-means clustering (FCM). In the feature extraction process, the wavelet and texture features are extracted from the segmented image with respect to DWT and GLCM features, respectively. Finally, the extracted features are subjected to a classification process via optimized Generative Adversarial Networks (GAN) classifier. Further, the weight of GAN will be fine-tuned via a new hybrid optimization model referred to as Tunicate Adopted Moth Flame (TAMF) algorithm. This tuning process enhances the network training to determine accurate classification results. The output gets the differentiated benign and malignant types.

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Abbreviations

ACO:

Ant colony optimization

ANN:

Artificial neural network

BN:

Bayesian Network

CLAHE:

Contrast Limited Adaptive Histogram Equalization

DWT:

Discrete wavelet transform

FCM:

Fuzzy C-means clustering

FDR:

False Discovery Rate

FNAC:

Fine-needle aspiration cytology repository for UCI machine learning

FOA:

Fruit fly optimization algorithm

FPR:

False positive rate

GA-MOO-NN:

Genetic algorithm-based multi-objective optimization of an artificial neural network classifier

GAN:

Generative adversarial networks

GLCM:

Gray level co-occurrence matrix

IABC-EMBOT:

Intelligent artificial bee colony and enhanced monarchy butterfly optimization technique

IMC:

Information measures of correlation

LF:

Levy flight

LFOA:

FOA enhanced by Lf strategy

LSSVM:

Least square support vector machine

MCC:

Matthews correlation coefficient

MFO:

Moth flame optimization

MKL:

Multiple kernel learning

NPV:

Negative predictive value

SVM:

Support vector machines

TAMF:

Tunicate adopted moth flame

TSA:

Tunicate swarm algorithm

WOA:

Whale optimization algorithm

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Authors and Affiliations

  1. ECE, UIT, RGPV, Gandhi Nagar, Bhopal, Madhya Pradesh, 462033, India

    Ekta Shivhare

  2. ECE, UIT, RGPV, Bhopal, India

    Vineeta Saxena

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  1. Ekta Shivhare
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  2. Vineeta Saxena
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Correspondence to Ekta Shivhare.

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Communicated by B. Xiao.

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Shivhare, E., Saxena, V. Optimized generative adversarial network based breast cancer diagnosis with wavelet and texture features. Multimedia Systems 28, 1639–1655 (2022). https://doi.org/10.1007/s00530-022-00911-z

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