A New Approach for Mammogram Image Classification Using Fractal Properties

Don, S.; Chung, Duckwon; Revathy, K.; Choi, Eunmi; Dai, Min

doi:10.2478/cait-2012-0013

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…The data in the Breast Cancer Wisconsin (Diagnostic) dataset is closely tied to research based around mammogram images. Research on this topic can also be found [18]. Tests have been made on models where dendritic neuron layers are inserted into perceptron based neural networks, on models where perceptron layers are completely swapped out for the dendritic neuron layers, and on neural networks implementing other types of layers.…”

Section: Results On the Undecorated Dendritic Neuron Modelmentioning

confidence: 99%

Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

Tomov,

Momcheva

2023

Cybernetics and Information Technologies

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Throughout the years neural networks have been based on the perceptron model of the artificial neuron. Attempts to stray from it are few to none. The perceptron simply works and that has discouraged research around other neuron models. New discoveries highlight the importance of dendrites in the neuron, but the perceptron model does not include them. This brings us to the goal of the paper which is to present and test different models of artificial neurons that utilize dendrites to create an artificial neuron that better represents the biological neuron. The authors propose two models. One is made with the purpose of testing the idea of the dendritic neuron. The distinguishing feature of the second model is that it implements activation functions after its dendrites. Results from the second model suggest that it performs as well as or even better than the perceptron model.

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Section: Results On the Undecorated Dendritic Neuron Modelmentioning

confidence: 99%

Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

Tomov,

Momcheva

2023

Cybernetics and Information Technologies

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“…Such complex objects usually possess the property of self-similarity, and this is one of the central concepts of fractal geometry. It has been found in the literature that features derived from fractal analysis play an important role in distinguishing between malignant and benign breast masses as malignant breast masses possess more complex textures than benign masses [ 7 , 32 ]. The two most commonly used techniques for calculating the fractal dimension are the box-counting method and the fractional Brownian motion model [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

Singh

Sharma

Singh

2022

Vis. Comput. Ind. Biomed. Art

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This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms. An improved machine learning-based framework was developed for this study. The proposed system was tested using 106 full field digital mammography images from the INbreast dataset, containing a total of 115 breast mass lesions. The proficiencies of individual and various combinations of computed textures and geometric features were investigated by evaluating their contributions towards attaining higher classification accuracies. Four state-of-the-art filter-based feature selection algorithms (Relief-F, Pearson correlation coefficient, neighborhood component analysis, and term variance) were employed to select the top 20 most discriminative features. The Relief-F algorithm outperformed other feature selection algorithms in terms of classification results by reporting 85.2% accuracy, 82.0% sensitivity, and 88.0% specificity. A set of nine most discriminative features were then selected, out of the earlier mentioned 20 features obtained using Relief-F, as a result of further simulations. The classification performances of six state-of-the-art machine learning classifiers, namely k-nearest neighbor (k-NN), support vector machine, decision tree, Naive Bayes, random forest, and ensemble tree, were investigated, and the obtained results revealed that the best classification results (accuracy = 90.4%, sensitivity = 92.0%, specificity = 88.0%) were obtained for the k-NN classifier with the number of neighbors having k = 5 and squared inverse distance weight. The key findings include the identification of the nine most discriminative features, that is, FD26 (Fourier Descriptor), Euler number, solidity, mean, FD14, FD13, periodicity, skewness, and contrast out of a pool of 125 texture and geometric features. The proposed results revealed that the selected nine features can be used for the classification of breast masses in mammograms.

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“…These measures can be used in the analysis of signals and images as informative summaries. In medical applications, fractal dimension and Hurst exponent are widely used for the analysis, characterization, and classification of mammogram images .…”

Section: Mammary Cancer Classification Using Complex Wavelet‐based Sementioning

confidence: 99%

Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process

et al. 2015

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Fractals are models of natural processes with many applications in medicine. The recent studies in medicine show that fractals can be applied for cancer detection and the description of pathological architecture of tumors. This fact is not surprising, as due to the irregular structure, cancerous cells can be interpreted as fractals. Inspired by Sierpinski carpet, we introduce a flexible parametric model of random carpets. Randomization is introduced by usage of binomial random variables. We provide an algorithm for estimation of parameters of the model and illustrate theoretical and practical issues in generation of Sierpinski gaskets and Hausdorff measure calculations. Stochastic geometry models can also serve as models for binary cancer images. Recently, a Boolean model was applied on the 200 images of mammary cancer tissue and 200 images of mastopathic tissue. Here, we describe the Quermass-interaction process, which can handle much more variations in the cancer data, and we apply it to the images. It was found out that mastopathic tissue deviates significantly stronger from Quermass-interaction process, which describes interactions among particles, than mammary cancer tissue does. The Quermass-interaction process serves as a model describing the tissue, which structure is broken to a certain level. However, random fractal model fits well for mastopathic tissue. We provide a novel discrimination method between mastopathic and mammary cancer tissue on the basis of complex wavelet-based self-similarity measure with classification rates more than 80%. Such similarity measure relates to Hurst exponent and fractional Brownian motions. The R package FractalParameterEstimation is developed and introduced in the paper.

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A New Approach for Mammogram Image Classification Using Fractal Properties

Cited by 9 publications

References 18 publications

Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

Multi-Activation Dendritic Neural Network (MA-DNN) Working Example of Dendritic-Based Artificial Neural Network

Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

Fractal and stochastic geometry inference for breast cancer: a case study with random fractal models and Quermass-interaction process

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