Classification of polycystic ovary based on ultrasound images using competitive neural network

Dewi, Ria May; Adiwijaya, Adiwijaya; Wisesty, Untari Novia; Jondri,

doi:10.1088/1742-6596/971/1/012005

Cited by 44 publications

(12 citation statements)

References 9 publications

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“…Normally, feature extraction and classification or stereology calculation is employed to detect PCO. R M Dewi et al [35] introduced a competitive neural network model to detect polycystic ovary from ultrasound images given as input. For feature extraction, Gabor wavelet model was used and the competitive neural network acted as the classifier.…”

Section: Related Work On Pcos Detectionmentioning

confidence: 99%

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Pulluparambil

Bhat

2021

IJHSP

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Purpose: Considered as the most common hormonal disorder among women, polycystic ovary syndrome or PCOS affects 1 in 10 reproductive aged women (18 - 44 years). Ultrasonography is applied for assessing the ovaries to detect PCOS. The patients affected by PCOS consist of 10-12 cysts present in the ovary, but more than 10 cysts are more enough to diagnose the disorder from the ultrasound images. Then, by examining the ultrasound the presence of follicles will be determined. Therefore, the image processing approaches have assisted to identify the characteristics like follicle size, number of follicles and structure to minimize the workload and time of doctors. PCOS do not have better treatment and effective diagnosis. This paper includes reviewing a summary of some of the researches that have been going in area of medical diagnosis. Based on the review, research gap, research agendas to carry out further research are identified. Approach: A detailed study on the algorithms used in medical image processing and classification. Findings: The study indicated that most of the classification of polycystic ovarian syndrome is done merely on the clinical data sets. The new hybrid methodology proposed will be more precise as both images and lifestyle are analysed. Originality: The type of data required for detection system are studied and the architecture and schematic diagram of a proposed system are included. Paper Type: Literature Review.

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Section: Related Work On Pcos Detectionmentioning

confidence: 99%

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Pulluparambil

Bhat

2021

IJHSP

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“…Dewi et al, [40] proposed a automatic system for follicle identification based on Gabor wavelet based feature estimation method and Competitive Neural Network which is the combination of Hemming Net and Max Net and such a combination leads to better classification rate. Kumar et al, [41] suggested median filter and histogram equalization for image preprocessing of Ultrasound imaging of ovaries and they performed a comparative analysis of particle swarm optimization, chaotic particle swarm optimization, pigeon, inspired optimization and Gaussian pigeon inspired optimization algorithms, and they adopted Dice and Jaccard coefficients for measuring the similarity.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of Polycystic Ovarian Syndrome: A Literature Survey

Saravanan

Sathiamoorthy

2018

AJEAT

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Polycystic ovarian syndrome is an endocrine issue attacking ladies at the age of reproduction. This indication has primarily found in ladies whose age is in the middle of 25 and 35. It is essential to diagnose and recognize diverse types of ovulatory failure that can add to infertility. There are numerous clarifications for ovulation failure. Without distinguishing the correct locality of the follicle, the risk seriousness of the patient can’t reveal. In line with this, many of the researchers focusing their research interest in PCOS. In this paper, literature review on polycystic ovarian syndrome using machine learning and image processing has exhibited.

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“…Ultrasound images of various regions of the body have been used in CAD to diagnose diferent types of illnesses that can threaten human life, such as breast cancer [17], hydronephrosis [18], and prostate cancer [19]. Moreover, many contributions have been carried out by other researchers in order to identify PCOS by using ultrasound images [4,13,14,16,[20][21][22][23][24][25]. Several machine learning and deep learning models have been implemented to perform ovary ultrasound image analyses for diagnosis systems, such as SVM [24], NB [22], CNN [20,21,25], and VGG-16 [16].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

Al-Amoudi

Khan

Aslam

et al. 2023

Applied Computational Intelligence and Soft Computing

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One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.

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Classification of polycystic ovary based on ultrasound images using competitive neural network

Cited by 44 publications

References 9 publications

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Medical Image Processing: Detection and Prediction of PCOS – A Systematic Literature Review

Detection of Polycystic Ovarian Syndrome: A Literature Survey

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

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