ISSN: 2520-4750 (Online), 2521-3040 (Print)

Ovarian Cyst Detection by Region Based Convolutional Neural Network in MATLAB

 Author (s)

Refat Noor Swarna

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 %.

Keywords: Ultrasound image, Ovarian Cyst, Detection, RCNN, MATLAB

 

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Title:Ovarian Cyst Detection by Region Based Convolutional Neural Network in MATLAB
Author:Refat Noor Swarna
Journal Name:International Journal of Science and Business
Website:ijsab.com
ISSN:ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI:https://doi.org/10.5281/zenodo.5816373
Media:Online
Volume:7
Issue:1
Issue publication (Year):2022
Acceptance Date:28/11/2021
Date of Publication:04/01/2022
PDF URL:https://ijsab.com/wp-content/uploads/873.pdf
Free download:Available
Page:24-33
First Page:24
Last Page:33
Paper Type:Research paper
Current Status:Published

 

Cite This Article:

Refat Noor Swarna(2022). Ovarian Cyst Detection by Region Based Convolutional Neural Network in MATLAB. International Journal of Science and Business, 7(1), 24-33. doi: https://doi.org/ 10.5281/zenodo.5816373

Retrieved from https://ijsab.com/wp-content/uploads/873.pdf

 

About Author (s)

Refat Noor Swarna, Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh.

 

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DOI: https://doi.org/10.5281/zenodo.5816373

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