A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

González-Patiño, David; Villuendas-Rey, Yenny; Cruz, Amadeo José Argüelles; Karray, Fakhri

doi:10.3390/app9214492

Cited by 12 publications

(8 citation statements)

References 44 publications

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“…In 2019 [52] a preliminary work was carried out using the first version of an algorithm based on the immune system. Good results were obtained, however in this new study, analysis with a larger number of datasets and a comparison with more classification algorithms was considered.…”

Section: Comparison Of Aisac Versus Well-known Learning Algorithmsmentioning

confidence: 99%

AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection

et al. 2020

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Early breast cancer diagnosis is crucial, as it can prevent further complications and save the life of the patient by treating the disease at its most curable stage. In this paper, we propose a new artificial immune system model for associative classification with competitive performance for breast cancer detection. The proposed model has its foundations in the biological immune system; it mimics the detection skills of the immune system to provide correct identification of antigens. The Wilcoxon test was used to identify the statistically significant differences between our proposal and other classification algorithms based on the same bio-inspired model. These statistical tests evidenced the enhanced performance shown by the proposed model by outperforming other immune-based algorithms. The proposed model proved to be competitive with respect to other well-known classification models. In addition, the model benefits from a low computational cost. The success of this model for classification tasks shows that swarm intelligence is useful for this kind of problem, and that it is not limited to optimization tasks.

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Section: Comparison Of Aisac Versus Well-known Learning Algorithmsmentioning

confidence: 99%

AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection

et al. 2020

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“…It also has an elitist survival strategy. Other bio-inspired algorithm used for clustering can be found in [55].…”

Section: B Genetic Algorithm Based Clusteringmentioning

confidence: 99%

Clustering mixed data using an Artificial Bee Colony

Cabrera-Venegas*,

Chávez-Castilla

2019

IJITEE

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 Abstract: In this paper, we have proposed a clustering technique which optimizes the total compactness and separation (measured using the Silhouette index) of the clusters. The proposed algorithm uses an Artificial Bee Colony (ABC) based optimization method as the underlying optimization criterion. We used similarity based prototypes as cluster centers. The proposed clustering technique is able to suitably handle mixed and incomplete data types in such a way that the original characteristics of the data are preserved. Assignment of points to different clusters is done based on a dissimilarity function rather than the Euclidean distance. Results on real-life data sets show that the proposed technique is well-suited to detect true partitioning from data sets. Results are compared with those obtained by four existing clustering techniques, one genetic algorithm based clustering technique (AGKA), the k-Prototypes (KP) algorithm, well-known based K-means clustering technique for similarity functions (KMSF) and a newly developed algorithm with dissimilarity based clustering technique (AD2011).

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“…Although compact set have been useful in Artificial Intelligence tasks, the definition of maximum similarity implies that each instance is only connected to its most similar one, not taking into account other instances that might be slightly less similar, but also very similar to the analysed instance [28][29][30][31][32].…”

Section: Clustering Based On  0 -Strong Compact Groupsmentioning

confidence: 99%

Using Extended Compact Sets to Cluster Educational Data

Rey-Benguría*

2019

IJITEE

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The pedagogical orientation to the families of children with behavioral deficiencies needs a differentiated approach, accorded to the particular characteristics of each family. To accomplish this task, the personnel in charge of family orientation in the Schools for children with affective-behavioral maladies, diagnose the families, to obtain the peculiarities of the familiar dynamics. After that, they need to obtain groups of similar families, in order to carry out a better orientation. The data of the familiar dynamics are mixed an incomplete, and the desired group number is not given. In this paper we extend the  0-compact sets structuralization to cluster the families according to their characteristics. We found five groups, each of them with distinctive familiar dynamics. With the five groups, the family orientation specialists designed a personalized strategy with a more coherent and adequate orientation.

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A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

Cited by 12 publications

References 44 publications

AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection

AISAC: An Artificial Immune System for Associative Classification Applied to Breast Cancer Detection

Clustering mixed data using an Artificial Bee Colony

Using Extended Compact Sets to Cluster Educational Data

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