Classifying Mammography Images by Using Fuzzy Cognitive Maps and a New Segmentation Algorithm

Amirkhani, Abdollah; Kolahdoozi, Mojtaba; Papageorgiou, Elpiniki I.; Mosavi, Mohammad Reza

doi:10.1007/978-3-319-77911-9_6

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…(b) End-to-end DL-based CAD system designs and (c) hybrid CAD system designs using deep feature extraction and classification using ML classifier. The conventional ML-based CAD system designs use various stages like feature extraction, feature selection as well as classification (Amirkhani et al, 2018;Bektas et al, 2018;Kumar et al, 2016). DL-based CAD system designs have also been popularly used for classification of mammographic masses using SFM images (Abbas, 2016;Abdelhafiz et al, 2019;Aboutallib et al, 2018;Agnes et al, 2019;Al-masni et al, 2018;Khamparia et al, 2021;Kumar et al, 2017;Lecon et al, 2015;Ravishankar et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers

2023

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Section: Introductionmentioning

confidence: 99%

Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers

2023

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“…In addition, an extension of FCMs to Fuzzy Cognitive Networks with functional weights was proposed for time series and pattern recognition applications; the functional weights of the model were determined, after exploiting fuzzy inference systems and polynomial approximators of various orders [6]. Classification of mammography images was performed in [7], combining an FCM with an introduced threshold based region growing algorithm. The proposed algorithm aimed to segment mammography images by striking a balance between the mean and variance of brightness intensity within a tumor region.…”

Section: Introductionmentioning

confidence: 99%

Automatic Fuzzy Graph Construction For Interpretable Image Classification

Sovatzidi

Vasilakakis

Iakovidis

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Interpretable machine learning models have recently received a considerable attention for their capability to provide understandable explanations of predictions evoked from complex systems. In this paper a novel automatic interpretable classification scheme is introduced based on a Fuzzy Cognitive Map (FCM). The proposed approach aims to address the problem of image classification using highlevel features, extracted from a Convolutional Neural Network (CNN). The proposed FCM constitutes a fuzzygraph structure for representing causal reasoning concerning semantic concepts of the real world, as these are depicted within different images. An advantage over current classifiers is that the proposed FCM embeds a mechanism for automatic determination of its structure from the datasets used in each experiment. More importantly, it provides interpretable classification results, whereas it is simple to implement. Experimental results using publicly available datasets show that the proposed approach efficiently extends the application of the traditional FCMs into image classification problem.

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“…T HE FUZZY cognitive maps (FCMs), which were introduced by Kosko [1], are widely applied to knowledge-based representation and reasoning, complex system modeling [2]- [4]; time-series forecasting [5], [6]; decision making [7]- [9]; classification patterns [10]- [12]; risk assessment and management [13], [14]; and others. The FCMs are the directed graphs with feedback loops, which consists of nodes and directed weights.…”

Section: Introductionmentioning

confidence: 99%

The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy

Feng

Pedrycz

et al. 2021

IEEE Trans. Cybern.

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Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the populationbased learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods.Index Terms-Fuzzy cognitive maps (FCMs), maximum entropy, noisy data, rapid and robust learning.

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Classifying Mammography Images by Using Fuzzy Cognitive Maps and a New Segmentation Algorithm

Cited by 10 publications

References 35 publications

Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers

Hybrid computer aided diagnostic system designs for screen film mammograms using DL‐based feature extraction and ML‐based classifiers

Automatic Fuzzy Graph Construction For Interpretable Image Classification

The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy

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