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RETRACTED ARTICLE: Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system

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This article was retracted on 06 June 2022

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Abstract

In recent years, breast cancer is recognized as critical disease that needs to be detected at early stage. Breast cancer can be detected either by mammography or biopsy technique. Biopsy is cost effective procedure for breast cancer detection. Fine needle aspiration (FNA) digital image is pre-processed and extracted features from it used for classification. Based on the parameters of feature attributes, they are classified into two classes: malignant (cancerous) and benign (not cancerous) tumor cell. Manual analysis and classification of this image is very difficult and challenging task. The need of automation for detecting and classifying these tumor cells in cost effective and accurate manner provokes many researchers in this field. With this aim we proposed PCA–LDA based feature extraction and reduction (FER) technique that reduce the original feature space to large extent and performing training over this reduced set that give excellent accuracy of 98.6%. For classification we use ANNFIS classifier that uses the neural-network concept with some fuzzy rule logic. We perform comparative performance analysis study amongst our proposed work over two other classifiers i.e. support vector machine (SVM) and multi-layer perceptron (MLP). The experimental result shows that proposed framework outperform over SVM and MLP with an accuracy of 98.6%.

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Authors and Affiliations

  1. Department of CSE, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Tamil Nadu, 629180, India

    R. Preetha & S. Vinila Jinny

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  1. R. Preetha
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  2. S. Vinila Jinny
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Correspondence to R. Preetha.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04063-w"

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Preetha, R., Jinny, S.V. RETRACTED ARTICLE: Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system. J Ambient Intell Human Comput 12, 7195–7204 (2021). https://doi.org/10.1007/s12652-020-02395-z

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