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A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features

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Abstract

In this paper, Levenberg–Marquardt feedforward MLP neural network (LMFFNN) was proposed to classify cervical cell images obtained from 100 patients including healthy, low-grade intraepithelial squamous lesion and high-grade intraepithelial squamous lesion cases. This neural network along with extracted cell image features is a new model for cervical cell image classification. The semiautomated cervical cancer diagnosis system is composed of two phases: image preprocessing/processing and feedforward MLP neural network. In the first stage, image preprocessing is done to reduce the existing noises without lowering the resolution. After that, image processing algorithms were applied to manually cropped cell images to achieve a linear plot which includes real components, were used as LMFFNN inputs for classification of cervical cell images. Based on the results, cervical cell images were classified successfully with 100 % correct classification rate using the proposed method. Moreover, the rates of sensitivity and specificity were calculated as 100 % using LMFFNN method. It was shown there was a good agreement between the expert decision and values gained from the ANN model.

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Conflicts of interest

The authors had no competing interests to declare in relation to this article.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Babak Sokouti & Siamak Haghipour

  2. Department of Pathology, Tabriz University of Medical Sciences, Tabriz, Iran

    Ali Dastranj Tabrizi

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  1. Babak Sokouti
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  2. Siamak Haghipour
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  3. Ali Dastranj Tabrizi
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Correspondence to Babak Sokouti.

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Sokouti, B., Haghipour, S. & Tabrizi, A.D. A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features. Neural Comput & Applic 24, 221–232 (2014). https://doi.org/10.1007/s00521-012-1220-y

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  • DOI: https://doi.org/10.1007/s00521-012-1220-y

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