PERFORMANCE ANALYSIS OF DOWN SYNDROME DETECTION SYSTEM USING ULTRA SOUND FETUS IMAGES AND DEEP LEARNING CLASSIFICATION METHODS
Abstract
Down Syndrome (DS) is a genetic disorder due to a partial copy of the 21 st chromosome in the fetus, usually detected by invasive techniques like Amniocentesis and Chorionic Villus Sampling (CVS) which have chances of miscarriage. This paper suggests a completely automated and computer-aided method of non-invasive detection of DS by ultrasound fetus (USF) images, as a way of overcoming the constraints of the conventional diagnostic methods. The proposed system applies Non-Sub Sampled Contourlet Transform (NSCT) in transforming the image, and extracting features and classifying them using a custom Convolutional Neural Network (CNET). The system was trained and tested with normal and abnormal USF images with considerable results. On the Mendeley data, the system had Fetal NT Sensitivity (FNSE) of 99.24, Fetal NT Specificity (FNSP) of 99.19, Fetal NT Accuracy (FNA) of 99.23, Fetal Positive predictive rate (FPPR) of 99.28, and Fetal Negative predictive rate (FNPR) of 99.24. In the case of Kaggle Fetus (KF) dataset, the system achieved FNSE of 99.24, FNSP of 99.33, FNA of 99.1, FPPR of 99.29, and FNPR of 99.26. Mendeley and KF datasets had the average Fetus Detection Time (FDT) of 0.51 ms and 0.46 ms, respectively. The results of this study prove that the proposed method performs better in terms of detection accuracy, sensitivity, and speed and is a promising tool to conduct early non-invasive screening of the fetus and minimize the use of invasive diagnostic methods.
All authors have read and agreed to the published version of the manuscript.
References
1.
van der Meij KR, Henneman L, Sistermans EA. Non-invasive prenatal testing for everybody or contingent screening. Prenatal Diagnosis. 2023 Apr 1;43(4):443-7.
2.
Xie HN, Wang N, He M, Zhang LH, Cai HM, Xian JB, Lin MF, Zheng J, Yang YZ. Using deep‐learning algorithms to classify fetal brain ultrasound images as normal or abnormal. Ultrasound in Obstetrics & Gynecology. 2020 Oct;56(4):579-87.
3.
Kumar S, Selvakumar K. Various Methods for Computing Risk Factors of Down Syndrome in Fetus. Archives of Computational Methods in Engineering. 2025 Jan;32(1):485-98.
4.
Ye C, Duan H, Liu M, Liu J, Xiang J, Yin Y, Zhou Q, Yang D, Yan R, Li R. The value of combined detailed first-trimester ultrasound–biochemical analysis for screening fetal aneuploidy in the era of non-invasive prenatal testing. Archives of Gynecology and Obstetrics. 2024 Aug;310(2):843-53.
5.
Micucci M, Iula A. Recent advances in machine learning applied to ultrasound imaging. Electronics. 2022 Jun 6;11(11):1800.
6.
Thomas MC, Arjunan SP. Deep learning measurement model to segment the nuchal translucency region for the early identification of down syndrome. Measurement Science Review. 2022 May 14;22(4):187-92.
7.
Bowden B, de Souza S, Puchades A, Williams K, Morgan S, Anderson S, Tucker D, Hillier S. Implementation of non‐invasive prenatal testing within a national UK antenatal screening programme: Impact on women’s choices. Prenatal Diagnosis. 2022 May;42(5):549-56.
8.
Yousefpour Shahrivar R, Karami F, Karami E. Enhancing fetal anomaly detection in ultrasonography images: a review of machine learning-based approaches. Biomimetics. 2023 Nov 2;8(7):519.
9.
Zhang M, Gao Y, Liang M, Wang Y, Guo L, Wu D, Xiao H, Lin L, Wang H, Liao S. Correlation between maternal serum biomarkers and the risk of fetal chromosome copy number variants: a single-center retrospective study. Archives of Gynecology and Obstetrics. 2024 Aug;310(2):933-42.
10.
Stallings EB, Isenburg JL, Rutkowski RE, Kirby RS, Nembhard WN, Sandidge T, Villavicencio S, Nguyen HH, McMahon DM, Nestoridi E, Pabst LJ. National population‐based estimates for major birth defects, 2016–2020. Birth defects research. 2024 Jan;116(1):e2301.
11.
Raza A, Munir K, Almutairi MS, Sehar R. Novel transfer learning based deep features for diagnosis of down syndrome in children using facial images. IEEE Access. 2024 Jan 29;12:16386-96.
12.
Tang J, Han J, Xue J, Zhen L, Yang X, Pan M, Hu L, Li R, Jiang Y, Zhang Y, Jing X. A deep-learning-based method can detect both common and rare genetic disorders in fetal ultrasound. Biomedicines. 2023 Jun 19;11(6):1756.
13.
Geppert J, Stinton C, Johnson S, Clarke A, Grammatopoulos D, Taylor‐Phillips S. Antenatal screening for fetal trisomies using microarray‐based cell‐free DNA testing: a systematic review and meta‐analysis. Prenatal Diagnosis. 2020 Mar;40(4):454-62.
14.
Devisri B, Kavitha M. Fetal growth analysis from ultrasound videos based on different biometrics using optimal segmentation and hybrid classifier. Statistics in Medicine. 2024 Feb 28;43(5):1019-47.
15.
Khan IU, Aslam N, Anis FM, Mirza S, AlOwayed A, Aljuaid RM, Bakr RM, Qahtani NH. Deep learningbased computer-aided classification of amniotic fluid using ultrasound images from Saudi Arabia. Big Data and Cognitive Computing. 2022 Oct 3;6(4):107.
16.
Abdullah D. Uncertainty-Aware Representation Learning with Physical Consistency for Robust Decision Processes in Large-Scale Data Systems. Journal of Scalable Data Engineering and Intelligent Computing. 2025 Sep 24:9-17.
17.
Koul AM, Ahmad F, Bhat A, Aein QU, Ahmad A, Reshi AA, Kaul RU. Unraveling Down syndrome: from genetic anomaly to artificial intelligence-enhanced diagnosis. Biomedicines. 2023 Dec 12;11(12):3284.
18.
Reshi AA, Shafi S, Qayoom I, Wani M, Parveen S, Ahmad A. Deep learning-based architecture for down syndrome assessment during early pregnancy using fetal ultrasound images. International Journal of Experimental Research and Review. 2024;38:182-93.
19.
Alzubaidi M, Agus M, Alyafei K, Althelaya KA, Shah U, Abd-Alrazaq A, Anbar M, Makhlouf M, Househ M. Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images. Iscience. 2022 Aug 19;25(8).
20.
Selvathi D, Chandralekha R. Fetal biometric based abnormality detection during prenatal development using deep learning techniques. Multidimensional systems and signal processing. 2022 Mar;33(1):1-5.
21.
Wang C, Yu L, Su J, Mahy T, Selis V, Yang C, Ma F. Down syndrome detection with swin transformer architecture. Biomedical Signal Processing and Control. 2023 Sep 1;86:105199.
22.
Liu L, Tang D, Li X, Ouyang Y. Automatic fetal ultrasound image segmentation of first trimester for measuring biometric parameters based on deep learning. Multimedia Tools and Applications. 2024 Mar;83(9):27283-304.
23.
Abdullah D. Recent advancements in nanoengineering for biomedical applications: A comprehensive review. Innovative Reviews in Engineering and Science. 2024;1(1):1-5.
24.
Mengistu AK, Assaye BT, Flatie AB, Mossie Z. Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence. BMC Medical Imaging. 2025 May 26;25(1):183.
25.
Saranya S, Sudha S. Certain Mathematical Investigations on Prenatal Down Syndrome Detection using ANFIS Classification Approach. Appl. Math. 2020;14(1):97-104.
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