RETRACTED ARTICLE: Modelling of F3I based feature selection approach for PCOS classification and prediction

Maheswari, K.; Baranidharan, Thangavel; Karthik, S.; Sumathi, T.

doi:10.1007/s12652-020-02199-1

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…Tis process aims to extract the clinical information from the hospital system for the patients whose ultrasound image has been collected in the previous step. Te features were selected with the help of expert opinions and by taking into account recent studies that identifed the signifcance of those attributes on PCOS diagnosis in some manner [11,22]. During the clinical data collection process for 391 patients whose ultrasound images were already available, we found there are a lot of missing data that require further fltration.…”

Section: Datasetmentioning

confidence: 99%

“…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%

“…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]. However, many studies used other kinds of data rather than ultrasound images to diagnose PCOS, such as clinical data [11,26,27] and ultrasound reports in text format [28].…”

Section: Introductionmentioning

confidence: 99%

“…However, the study of [25] used an open-source dataset, but the study of [25]contained 3D images which is not suitable for our study. Moreover, most of these studies, such as [4,22], and [21], used a small-size dataset to build their model. In general, it has been noticed that there is a shortage of diagnosed PCOS by using ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…The second experiment of this paper use three classifiers as that can be measured by using common performance evaluation matrices like Precision [59], Recall or Sensitivity [60], Specificity [59], Accuracy [60], F1-score [60], AUC or Balanced Accuracy (BA) [61], Mathew's Correlation Coefficient (MCC) [59], Kappa Index [62], Critical Success Index (CSI) [63], Bookmaker Informedness (BM) [64] and Markedness (MK) [64]. Audio/Video and Male/Female classifications models are evaluated by these performance metrices that are illustrated in Tables 5 and 6.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

et al. 2021

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Recently Noninvasive Electroencephalogram (EEG) systems are gaining much attention. Brain-computer Interface (BCI) systems rely on EEG analysis to identify the mental state of the user, change in cognitive state and response to the events. Motor Execution (ME) is a very important control paradigm. This paper introduces a robust and useful User-Independent Hybrid Brain-computer Interface (UIHBCI) model to classify signals from fourteen EEG channels that are used to record the reactions of the brain neurons of nine subjects. Through this study the researchers identified relevant multisensory features of multi-channel EEG that represent the specific mental processes depending on two different evaluation models (Audio/Video) and (Male/Female). The Deep Belief Network (DBN) was applied independently on the two models where, the overall achieved classification rates were better in ME classification compared to the state of art. For evaluation four models were tested in addition to the proposed model, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Brain-computer Interface Lower-Limb Motor Recovery (BCI LLMR) and Hybrid Steady-State Visual Evoked Potential Rapid Serial Visual Presentation Brain-computer Interface (Hybrid SSVEP-RSVP BCI). Results indicated the proposed model, LDA, SVM, BCI LLMR and Hybrid SSVEP-RSVP BCI accuracies for (A/V) model are 94.44%, 66.67%, 61.11%, 83.33% and 89.67% respectively, while for (M/F) model, the overall accuracies are 94.44%, 88.89%, 83.331%, 85.44% and 89.45%. Finally, the proposed model achieved superiority over the state of art algorithms in both (A/V) and (M/F) models. INDEX TERMS Deep neural networks, independent component analysis, machine learning, motor execution.

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An Improved Technique for Risk Prediction of Polycystic Ovary Syndrome (PCOS) Using Feature Selection and Machine Learning

Aggarwal

Shukla

Saxena

et al. 2023

Lecture Notes in Electrical Engineering

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RETRACTED ARTICLE: Modelling of F3I based feature selection approach for PCOS classification and prediction

Cited by 19 publications

References 27 publications

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

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

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

An Improved Technique for Risk Prediction of Polycystic Ovary Syndrome (PCOS) Using Feature Selection and Machine Learning

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