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Ovarian Cyst Detection by Region Based Convolutional Neural Network in MATLAB

Author

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  • Refat Noor Swarna

    (Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh)

Abstract

For female reproductive system ovaries are one of the most important parts. The two ovaries in female body mainly are to produce ovum and sex hormones. Nowadays, it has become very common of affecting cyst at ovaries which can lead to infertility commonly and widely even cancer. That’s why it is actually very important to detect and treat as early as possible. For the increasing rate of ovarian cyst cases raises anxiety towards women and the people of poor medical facilities areas are facing rapid growth of ovarian cancer because of late diagnosis. The main purpose of this research is to detect very fast and even small areas from ultrasound images whether the ovaries are cyst affected or not. The proposed methodology is the implementation of regions with convolutional neural networks (RCNN) on real patients’ ultrasound images in MATLAB platform. Both cystic and non-cystic images are used for detection and the mean accuracy of detecting cyst is 94.3 %.

Suggested Citation

  • Refat Noor Swarna, 2022. "Ovarian Cyst Detection by Region Based Convolutional Neural Network in MATLAB," International Journal of Science and Business, IJSAB International, vol. 7(1), pages 24-33.
    • Handle: RePEc:aif:journl:v:7:y:2022:i:1:p:24-33
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