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

Pulluparambil, Siji Jose; Bhat, Subrahmanya

doi:10.47992/ijhsp.2581.6411.0075

Cited by 19 publications

(10 citation statements)

References 46 publications

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“…The most prominent practice used by physicians to accurately diagnose PCOS is to examine ultrasound images of the ovaries to examine for the existence of multiple follicular cysts and hence determine whether or not the patient has PCOS 10 . Ultrasound imaging is a well-known diagnostic technology that uses ultrasound waves generated by a transducer to create images of human body parts that provide real-time, precise anatomical and physiological information 11 .…”

Section: Related Workmentioning

confidence: 99%

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

Suha

Islam

2022

Sci Rep

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Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the “VGGNet16” pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being “XGBoost” model as image classifier with an accuracy of 99.89% for classification.

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Section: Related Workmentioning

confidence: 99%

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

Suha

Islam

2022

Sci Rep

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“…Many research works have been accomplished related to the TLDIs processing and analysis [1][2][3][4][5][6][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Some of the works are mentioned as follows.…”

Section: Related Workmentioning

confidence: 99%

A Machine Intelligent Based Approach for the Classification and Analysis of Tomato Leaf Disease Images

Jena

2022

IJHSP

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Purpose: Vegetable production plays a vital role for the existence of human society. It is very much essential for the proper care of vegetable plants for better production of vegetables. However, vegetable plant leaf disease is a major concern in the current scenario. Tomato leaf disease is one of them. So, preventive measures should be taken to avoid the rise of tomatoes and other leaf diseases at the earliest for better production of vegetables. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of tomato leaf disease images (TLDIs) into the bacterial spot (BS), early blight (EB), late blight (LB), leaf mold (LM), septoria leaf spot (SLS), tomato mosaic virus (TMV), tomato yellow leaf curl virus (TYLCV) and healthy (HL) types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1600 TLDIs having 200 numbers of each type such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of TLDIs into several types such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

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“…So, the classification of HFIs into HFWMIs and HFWOMIs types is a crucial requirement in this situation. ML [24][25][26][27][28][29] can be considered as a solution for the classification of HFIs into HFWMIs and HFWOMIs categories. The ML based methods can be broadly classified as supervised and unsupervised.…”

Section: Introductionmentioning

confidence: 99%

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

Jena¹,

K²,

Thinakaran³

2022

STAIQC

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The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.

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

Cited by 19 publications

References 46 publications

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image

A Machine Intelligent Based Approach for the Classification and Analysis of Tomato Leaf Disease Images

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

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