Abstract
PCOS-polycystic ovary syndrome is one of the prevalent hormonal disorders which has currently affected women populations around the age group of 22–45, in their reproductive cycle. It has been widely observed that PCOS leads to infertility. Diagnosis of infertile has proceeded by using ultrasound images of follicles present in the ovary and further examined by the features like the size of the follicles, number of follicles, age group of patients, and the hormonal test. Based on the features, ovaries are classified into three categories like Normal ovary, Cystic ovary, and PolyCystic ovary. Usually, the diameter of a follicle is more than 2–9 mm, and the count of the follicles is more than 12, then it is considered polycystic ovary. In this paper, the classification of the ovarian cyst is implemented by using the regularized CNN method. In additionally, the justification of the classification process also improved with the data augmentation method and more droplet layer techniques for better accuracy. In the proposed algorithm, the performance of the combined procedure is evaluated with the objective type of metrics and shows the accurate detection of the follicle and leads to conclude the classification of ovarian cyst.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee TT, Raush ME (2012) Polycystic ovarian syndrome: role of imaging in diagnosis. Radiographic.rsna.org 32:1643–1657. https://doi.org/10.1148/rg/326125503
Padmapriya B, Kesavamurthy T (2015) Diagnostic tool for PCOS classification. In: IFMBE Proceedings, vol 52. Springer International Publishing, Switzerland. https://doi.org/10.1007/978-3-319-19452-3_48
Tajbakhsh N, Shin JY, Gurudu SR, Todd Hurst R, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning. IEEE Trans Med Imaging 53(5):0278–0062
Denny A, Raj A, Ram AAM, George R (2019) i-HOPE: detection and prediction system for polycystic ovary syndrome (PCOS) using machine learning techniques. IEEE. 978-1-7281-1895-6/19
Legro RS, Arslanian SA, Ehrmann DA, Hoeger KM, Hassan Murad M, Pasquali R, Welt CK (2013) Diagnosis and treatment of polycystic ovary syndrome: an endocrine society clinical practice guideline. J Clin Endocrinol 98(12):4565–4592. https://doi.org/10.1210/jc.2013-2350
Goodman NF, Cobin RH, Futterweit W, Glueck JS, Legro RS, Carmina E (2015) American Association of Clinical Endocrinologists American College of Endocrinology, and antrogen excess and PCOS society disease state clinic review: guide to the best practices in the evaluation and treatment of polycystic ovary syndrome. Part-2 Endocr Pract 21
Adiwijaya, Purnama B, Hasyim A, Septiani MD, Wisesty UN, Astuti W (2015) Follicle detection on the USG images to support determination of polycystic ovary syndrome. J Phys Conf Ser 622(2015):012027
Wisesty UN, Nasri J, Adiwijaya (2017) Modified backpropagation algorithm for polycystic ovary syndrome detection based on ultrasound images. In: Recent advances on soft computing and data mining. Advances in intelligent systems and computing, vol 549. Springer International Publishing AG. https://doi.org/10.1007/978-3-319-51281-5_15
Hiremath PS, Tegnoor JR (2013) Follicle detection and ovarian classification in digital ultrasound images of ovaries. In: Advancements and breakthroughs in ultrasound imaging. https://doi.org/10.5772/56518
Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performance convolutional neural networks for image classification. In: Twenty second international joint conference on artificial intelligence
Nii M, Kato Y, Morimoto M, Kobashi S, Kamiura N (2018) Ovarian follicle classification using convolutional neural networks from ultrasound scanning images. Int J Comput Vis Sign Process 8(1). ISSN: 2186-1390
Sungheetha A, Rajesh Sharma R (2021) Design an early detection and classification for diabetic retinopathy by deep feature extraction based convolution neural network. J Trends Comput Sci Smart Technol (TCSST) 03(02):2582–4104
Dewi RM, Adiwijaya, Wisesty UN, Jondri (2018) Classification of polycystic ovary based on ultrasound images using competitive neural network. In: International conference on data and information science. https://doi.org/10.1088/1742-6596/971/1/012005
Vikas B, Radhika Y, Vineesha K (2021) Detection of polycystic ovarian syndrome using convolutional neural networks. Int J Curr Res Rev 13(06)
Masko PHD (2015) The impact of imbalanced training data for convolutional neural networks. Degree project, in computer science, first level Stockholm, Sweden
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Priya, N., Jeevitha, S. (2022). Classification of Ovarian Cyst Using Regularized Convolution Neural Network with Data Augmentation Techniques. In: Shakya, S., Du, KL., Haoxiang, W. (eds) Proceedings of Second International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 351. Springer, Singapore. https://doi.org/10.1007/978-981-16-7657-4_17
Download citation
DOI: https://doi.org/10.1007/978-981-16-7657-4_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7656-7
Online ISBN: 978-981-16-7657-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)