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Detection of Ovarian Cyst in Ultrasound Images Using Fine-Tuned VGG-16 Deep Learning Network

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Abstract

Ovaries play a vital role in the female reproductive system as they are responsible for the production of egg or ovum required during the fertilization. The female ovaries very often get affected with cyst. An enlarged ovarian cyst can lead to torsion, infertility and even cancer. Therefore, it is very important to diagnose it as soon as possible. For the diagnosis of an ovarian cyst, ultrasound test is conducted. We collected the sample ultrasound images of ovaries of different women and detected whether ovarian cyst is present or not. The proposed work employs the traditional VGG-16 model fine-tuned with our very own dataset of ultrasound images. A VGG-16 model is a 16-layer deep learning neural network trained on ImageNet dataset. Fine-tuning is done by modifying the last four layers of VGG-16 network. Our model is able to determine whether the ultrasound images shows ovarian cyst or not. An accuracy of 92.11% is obtained. The accuracy and loss curves are also plotted for the proposed model.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Sakshi Srivastava & Prince Kumar

  2. Department of Biotechnology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Vaishali Chaudhry

  3. Technology Business Incubator, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Anuj Singh

Authors
  1. Sakshi Srivastava
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  2. Prince Kumar
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  3. Vaishali Chaudhry
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  4. Anuj Singh
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Correspondence to Sakshi Srivastava.

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This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.

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Srivastava, S., Kumar, P., Chaudhry, V. et al. Detection of Ovarian Cyst in Ultrasound Images Using Fine-Tuned VGG-16 Deep Learning Network. SN COMPUT. SCI. 1, 81 (2020). https://doi.org/10.1007/s42979-020-0109-6

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  • DOI: https://doi.org/10.1007/s42979-020-0109-6

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