[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123840-en":3,"doc-seo-123840-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123840,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","A Machine Learning based Improvised Follicle Polycystic Ovarian Detection Through Ultrasound Images Through IOT","Polycystic ovarian syndrome (PCOS) is an endocrine disorder affecting women of reproductive age, where diagnosis commonly relies on detecting multiple small ovarian follicles using ultrasound imaging. Manual follicle detection remains time-consuming, subjective, and error-prone. This study presents an improvised follicle-based PCOS detection pipeline using machine learning (Random Forest and Logistic Regression) on sequences of ultrasound images. Pre-processing is performed via IoT, followed by segmentation and follicle feature extraction, then classification into normal vs. PCOS. Experiments on 400 images from 50 patients yield 93.75% accuracy and AUC 0.96, with improved metrics over state-of-the-art methods, plus quantitative severity estimation from detected follicle count and size.","A Machine Learning based Improvised Follicle Polycystic Ovarian Detection Through Ultrasound  \nImages Through IOT  \nKachibhotla Srinivas,  \nDepartment of Electrical, Electronics and Communication Engineering, Research scholar , GITAM (deemed to be University),  \nHyderabad, [kbhotlas02@gmail.com](kbhotlas02@gmail.com)  \nPrasantha R. Mudimela,  \nProfessor, Department of Electrical, Electronics and Communication Engineering, GITAM (deemed to be University), Hyderabad,  \n[pmudimel@gitam.edu](pmudimel@gitam.edu)  \nAbstract :  \nPolycystic ovarian syndrome which is commonly called as PCOS is a endocrine malfunction affecting women of reproductive age. Its diagnosis involves in detection of multiple small follicles mainly in the ovaries through ultrasound imaging. However, manual detection is time-consuming, subjective, and prone to errors. Hence, this study proposes an improvised follicle PCOS detection method using machine learning(Random Forest and Logistic Regression) from a sequence of given ultrasound images. The proposed method involves pre-processing the ultrasound images through IoT, followed by segmenting and extracting follicle features. Subsequently, a machine learning model is trained to classify the extracted features as normal or PCOS cases. The proposed method's performance is evaluated on a dataset of 400 ultrasound images from 50 patients, including 25 PCOS cases and 25 healthy controls. The experimental results demonstrate that the proposed method achieves a high classification accuracy of 93.75% and an AUC of 0.96. In addition, the proposed methodology outperforms in comparison with the state-of-the-art PCOS detection methods in terms of accuracy, sensitivity, specificity, and AUC. The proposed method also provides a quantitative measure of the severity of PCOS based on the number and size of the follicles detected.  \nKeywords : Machine Learning, Ultrasound images, Polycystic Ovarian Syndrome (PCOS), Ovarian follicles, IoT Sensors.  \nIntroduction :  \npolycystic Ovary Syndrome (PCOS) is a common reproductive endocrine disorder among women of reproductive age, with a prevalence of up to 15% globally [1] PCOS is characterized by the presence of multiple cystson the ovaries, along with other clinical and biochemical manifestations such as irregular menstrual cycles, hirsutism, acne, and insulin resistance[2] . The diagnosis of PCOS is based on clinical presentation and laboratory tests, including hormonal assays and ultrasound imaging. Among these, ultrasound imaging is an essential tool for the diagnosis of PCOS, as it can detect the presence of cysts and other morphological changes in the ovaries.Conventional ultrasound imaging indiagnosingthe PCOS involves the use of static images, which are obtained by capturing a snapshot of the ovaries during a single examination[3-5] . However, PCOS is a dynamic disorder, and the presence and morphology of cysts can vary over time, depending on various factors such as hormonal fluctuations and treatment  \ninterventions. Therefore, a more comprehensive and accurate method for the diagnosis of PCOS is needed, which can capture the dynamic nature of the disease and provide amore detailed assessment of the ovaries. Currently, PCOS diagnosis is typically made through a combination of clinical symptoms, physical examination, and laboratory tests[6,7] . However, the gold standard for diagnosis is the presence of polycystic ovaries detected by ultrasound.Ultrasound imaging is a non-invasive diagnostic tool that uses high-frequency sound waves to produce images of the inside of the body. It is widely used in the diagnosis of various medical conditions, including PCOS. Conventional ultrasound imaging for the PCOS diagnosis involves the use of static images, which are obtained by capturing a snapshot of the ovaries during a single examination. However, PCOS is a dynamic disorder, and the presence and morphology of cysts can vary over time, depending on various factors such as hormonal fluc","cbCaihUb1GBaXZaF","https://ap.wps.com/l/cbCaihUb1GBaXZaF","pdf",453754,1,10,"English","en",105,"# Introduction\n## Conventional ultrasound and its limitations\n## Rotterdam criteria and subjectivity\n## Motivation for objective ML-based detection\n# Proposed methodology\n## IoT-based ultrasound pre-processing\n## Segmentation and follicle feature extraction\n## Machine learning classification (Random Forest, Logistic Regression)\n# Experimental evaluation\n## Dataset and protocol\n## Metrics and reported performance\n## Comparison with state-of-the-art\n# Quantitative PCOS severity estimation","[{\"question\":\"Why is manual PCOS detection from ultrasound images difficult?\",\"answer\":\"Manual detection is time-consuming, subjective to the interpreting physician, and prone to errors due to variability in visual interpretation.\"},{\"question\":\"What role does IoT play in the proposed PCOS detection pipeline?\",\"answer\":\"IoT is used for ultrasound image pre-processing before segmentation and feature extraction, supporting the improvised detection workflow.\"},{\"question\":\"How are the machine learning models evaluated in the study?\",\"answer\":\"The pipeline is trained and tested on a dataset of 400 ultrasound images from 50 patients, with results reported using classification accuracy and AUC, achieving 93.75% accuracy and AUC 0.96.\"}]","A Machine Learning based Improvised Follicle Polycystic Ovarian Detection Through Ultrasound Images Through IOT | 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is manual PCOS detection from ultrasound images difficult?","Question",{"text":76,"@type":77},"Manual detection is time-consuming, subjective to the interpreting physician, and prone to errors due to variability in visual interpretation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role does IoT play in the proposed PCOS detection pipeline?",{"text":81,"@type":77},"IoT is used for ultrasound image pre-processing before segmentation and feature extraction, supporting the improvised detection workflow.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the machine learning models evaluated in the study?",{"text":85,"@type":77},"The pipeline is trained and tested on a dataset of 400 ultrasound images from 50 patients, with results reported using classification accuracy and AUC, achieving 93.75% accuracy and AUC 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