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      PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images


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          Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex classification model that can facilitate efficient screening and reduce misclassification.


          We have developed a novel feature engineering model termed PFP-LHCINCA that employs pyramidal fixed-size patch generation with average pooling-based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi-square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter-derived k-nearest neighbor-based misclassification rates. The model was trained and tested on a sizeable expert-labeled dataset comprising 339 males' and 332 females' fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally.


          Standard model performance metrics were compared using five shallow classifiers—k-nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM)—with the hyperparameters tuned using a Bayesian optimizer. The PFP-LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers.


          US-based fetal sex classification is feasible and accurate using the presented PFP-LHCINCA model. The salutary results support its clinical use for fetal US image screening for sex classification. The model architecture can be modified into deep learning models for training larger datasets.

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          Most cited references23

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          ImageNet classification with deep convolutional neural networks

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            The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

            Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.
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              A survey of decision tree classifier methodology


                Author and article information

                Contrast Media Mol Imaging
                Contrast Media Mol Imaging
                Contrast Media & Molecular Imaging
                18 May 2022
                : 2022
                1Department of Radiology, Adıyaman Training and Research Hospital, Adiyaman 1164, Turkey
                2Department of Obstetrics and Gynecology, Malatya Turgut Ozal University Training and Research Hospital, Malatya 44330, Turkey
                3Department of Obstetrics and Gynecology, Adıyaman Gozde Hospital, Adiyaman 1164, Turkey
                4School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia
                5Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
                6Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
                7Department of Cardiology, National Heart Centre Singapore, Bukit 169609, Singapore
                8Duke-NUS Medical School, Bukit 169857, Singapore
                9Rathinam College of Engineering, Coimbatore, India
                10Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Bukit 599489, Singapore
                11Department of Biomedical Engineering, School of Science and Technology, SUSS University, Bukit 599491, Singapore
                12Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 180-8629, Taiwan
                Author notes

                Academic Editor: Mohammad Farukh Hashmi

                Copyright © 2022 Ela Kaplan et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article


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