Automated Polycystic Ovarian Syndrome Identification with Follicle Recognition

Madhumitha, J.; Kalaiyarasi, M.; Ram, S. Sakthiya

doi:10.1109/icspc51351.2021.9451720

Cited by 21 publications

(4 citation statements)

References 9 publications

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“…De igual manera (Denny et al, 2019) trabajo con datos que pueden ser considerados como booleanos dentro de su conjunto de datos. (Madhumitha et al, 2021) y (Mubasher Hassan et al, 2020 quienes utilizaron datos obtenidos de imágenes, lograron generar algoritmos competitivos, sin embargo, se puede notar una diferencia en el F1 score de sus métricas con las generadas en el presente documento.…”

Section: Resultados Y Discusiónunclassified

Predicción de Ovario Poliquístico Aplicando Técnicas de Machine Learning

Cañedo Figueroa,

Blancarte Flores,

Guerra Hernández

et al. 2023

TECNOCIENCIA Chih.

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El Síndrome de Ovario Poliquístico (SOP) es una de las endocrinopatías más comunes entre las mujeres que se encuentran en edad reproductiva. Hay estudios que exponen que esta patología afecta entre el 3-15% de toda la población femenina. En este documento se describe el uso y la comparación de tres algoritmos de aprendizaje máquina para realizar un diagnóstico del SOP contemplado 18 características extraídos de la base de datos “PCOS Dataset” alojada en la plataforma Kaggle.com. Se diseñaron una Red Neuronal Artificial (RNA) con un 97.5% de F1, un algoritmo Bayesiano con un 97.6% de F1 y un algoritmo de los K- Vecinos más Cercanos (KNN por sus siglas en inglés) con un 100% de F1. Dejando un antecedente de la utilidad de los algoritmos de aprendizaje máquina para su aplicación en el diagnóstico de la mencionada anomalía.

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Section: Resultados Y Discusiónunclassified

Predicción de Ovario Poliquístico Aplicando Técnicas de Machine Learning

Cañedo Figueroa,

Blancarte Flores,

Guerra Hernández

et al. 2023

TECNOCIENCIA Chih.

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“…The obtained area under the curve score is 0.72. Madhumitha et al, [14] proposed an image segmentation model related to the region of interest in ultrasound images and also segment the background from the images. Morphological operation-based segmentation has been used to detect follicles.…”

Section: Related Workmentioning

confidence: 99%

“…where, 𝑋𝑋𝑋𝑋 𝑖𝑖𝑖𝑖,𝑗𝑗𝑗𝑗 -decision variable of ith solution in jth position, 𝑋𝑋𝑋𝑋 𝑗𝑗𝑗𝑗 𝑐𝑐𝑐𝑐𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑑𝑑𝑑𝑑 𝑋𝑋𝑋𝑋 𝑗𝑗𝑗𝑗 𝑙𝑙𝑙𝑙 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 is the upper and lower bound of jth position decision variable, r is the random variable in the range 0 to 1 and d is the total number of decision variables. The set of solutions for all N is generated and stored in X as in Equation ( 7) [14] 1,1…”

Section: Rsa Optimization Phasementioning

confidence: 99%

“…Machine and deep learning algorithms are widely used for this segmentation and diagnosis process recently. Madhumitha et al, [14] developed a PCOS prediction system using Morphological operation-based follicle segmentation. For classification, machine learning algorithms such as Support Vector Machine (SVM), K nearest neighbor (knn), and Logistic Regression (LR) approaches are used.…”

Section: Introductionmentioning

confidence: 99%

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Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

Sheikdavood¹,

Bala²

2023

ITC

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Polycystic ovary syndrome (PCOS) is a disorder in the female ovary caused because of reproductive age group hormonal changes. PCOS is a different follicle that is formed in the ovary and is termed an endocrine disorder. This disorder’s effects are often linked with clinical symptoms such as arteries, acne, hirsutism, diabetes, cardiovascular disease, and chronic infertility. It is mainly associated with type 2 diabetes, obesity with high cholesterol. This must be diagnosed at an earlier stage for avoiding other related diseases. To ensure infertility, various kinds of ovulatory failures must be significantly diagnosed and recognized. The physicians manually determine the PCOS using ultrasound images, but it is inefficient to declare whether it is a simple cyst, PCOS, or cancer cyst. This manual detection is prone to trying errors. In this paper, PCOS detection is performed through a series of processes such as preprocessing, segmentation, feature selection, and classification. The speckle noise is removed in preprocessing, and the images are enhanced for further processing. The proposed improved adaptive K-means with reptile search algorithm (IAKmeans-RSA) has been utilized for cyst segmentation and follicles recognition. The relevant features from the segmented images are extracted using a convolutional neural network (CNN). Finally, the classification is performed using the Deep Neural Network (DNN) approach. The proposed system efficiently diagnosed PCOS through cyst detection from the input images. The algorithm’s efficiency compared with existing methods shows that the proposed model is superior in segmenting and diagnosing PCOS.

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A Comprehensive Review of Predicting Lifestyle-Based Disease Specifically PCOS Among Women Using Data Mining and Machine Learning Approaches

Gupta,

Suresh

2024

Lecture Notes in Networks and Systems

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Automated Polycystic Ovarian Syndrome Identification with Follicle Recognition

Cited by 21 publications

References 9 publications

Predicción de Ovario Poliquístico Aplicando Técnicas de Machine Learning

Predicción de Ovario Poliquístico Aplicando Técnicas de Machine Learning

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

A Comprehensive Review of Predicting Lifestyle-Based Disease Specifically PCOS Among Women Using Data Mining and Machine Learning Approaches

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