Using Transfer Learning Approach for Down Syndrome Features Extraction and Data Augmentation for Data Expansion
Abstract
Purpose: People with Down Syndrome must be served special because they have an intellectual disability with abnormality in memory and learning, so, creating a model for DS recognition may provide safe services to them, using the transfer learning technique can improve high metrics with a small dataset, depending on previous knowledge, there is no available Down syndrome dataset, one can use to train.
Materials and Methods: A new dataset is created by gathering images, two classes (Down=209 images, non-Down=214 images), and then expanding this dataset using Augmentation to be the final dataset 892 images (Down=415images, Non-Down=477 images. Finally, using a suitable training model, in this work, Xception and Resnet models are used, the pretrained models are trained on Imagenet dataset which consists of (1000) classes.
Results: By using Xception model and Resnet model, it concluded that when using Resnet model the accuracy = 95.93% and the loss function =0.16, while by using Xception model, the accuracy =96.57% and the loss function =0.12.
Conclusion: A transfer learning is used, to overcome the suitability of dataset size and minimize the cost of training, and time processing the accuracy and loss function is good when using Xception model, in addition, the Xception metrics are the best by comparing with the previous studies.
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2- L. A. Alexandre, “3D Object Recognition using Convolutional Neural Networks with Transfer Learning between Input Channels.”
3- A. G. Mahmoud, A. M. Hasan, and N. M. Hassan, "Convolutional neural networks framework for human hand gesture recognition." Bulletin of Electrical Engineering and Informatics, vol. 10, no. 4, pp. 2223–2230, Aug. (2021), doi: 10.11591/EEI.V10I4.2926.
4- Z. Yang et al., "Deep transfer learning for military object recognition under small training set condition." Neural Comput Appl, vol. 31, no. 10, pp. 6469–6478, Oct. (2019), doi: 10.1007/s00521-018-3468-3.
5- F. F. Alkhalid, "The effect of optimizers in fingerprint classification model utilizing deep learning." Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 2, pp. 1098–1102, Nov. (2020), doi: 10.11591/ijeecs.v20.i2.pp1098-1102.
6- M. Hon and N. Khan, "Towards Alzheimer’s Disease Classification through Transfer Learning." Nov. (2017), [Online]. Available: http://arxiv.org/abs/1711.11117
7- M. Kirchler et al., "TRANSFERGWAS: GWAS OF IMAGES USING DEEP TRANSFER LEARNING", doi: 10.1101/2021.10.22.465430.
8- B. Maschler, H. Vietz, H. Tercan, C. Bitter, T. Meisen, and M. Weyrich, "Insights and Example Use Cases on Industrial Transfer Learning," in Procedia CIRP, Elsevier B.V., pp. 511–516, (2022). doi: 10.1016/j.procir.2022.05.017.
9- F. Mattioli, C. Porcaro, and G. Baldassarre, "A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface," J Neural Eng, vol. 18, no. 6, Dec. (2021), doi: 10.1088/1741-2552/ac4430.
10- F. Akhbardeh, M. Zampieri, C. O. Alm, and T. Desell, "Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets," (2022).
11- T. M. Ghazal et al., "Alzheimer disease detection empowered with transfer learning," Computers, Materials and Continua, vol. 70, no. 3, pp. 5005–5019, (2022), doi: 10.32604/cmc.2022.020866.
12- X. Zhang, J. Zhou, W. Sun, and S. K. Jha, "A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis," Computers, Materials and Continua, vol. 72, no. 1, pp. 1123–1137, (2022), doi: 10.32604/cmc.2022.024589.
13- P. Modiya and S. Vahora, "International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Brain Tumor Detection Using Transfer Learning with Dimensionality Reduction Method," Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE, vol.10, no. 2, pp. 201–206, (2022), doi: 10.18201/ijisae.
14- J. L. Natoli, D. L. Ackerman, S. Mcdermott, and J. G. Edwards, "Prenatal diagnosis of Down syndrome: A systematic review of termination rates (1995-2011)," Prenat Diagn, vol. 32, no. 2, pp. 142–153, Feb. (2012), doi: 10.1002/pd.2910.
15- E. A. Malt et al., "Helse og sykdom hos voksne med Downs syndrom 290-4.", (2013). [Online]. Available: www.tidsskriftet.no
16- M. E. Weijerman and J. P. De Winter, "Clinical practice: The care of children with Down syndrome," European Journal of Pediatrics, vol. 169, no. 12. pp. 1445–1452, Dec. (2010). doi: 10.1007/s00431-010-1253-0.
17- J. K. Morris, "Revised estimates of the maternal age specific live birth prevalence of Down’s syndrome." J Med Screen;9(1):2-6, (2002), doi: 10.1136/jms.9.1.2. [Online]. Available: www.jmedscreen.com
18- K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Dec. (2015), [Online]. Available: http://arxiv.org/abs/1512.03385
19- B. Feng et al., "Bi-stream CNN Down Syndrome screening model based on genotyping array," BMC Med Genomics, vol. 11, Nov. (2018), doi: 10.1186/s12920-018-0416-0.
20- J. J. Moreno Escobar, O. Morales Matamoros, E. Y. Aguilar del Villar, H. Quintana Espinosa, and L. Chanona Hernández, "DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy," Healthcare (Switzerland), vol. 11, no. 16, Aug. (2023), doi: 10.3390/healthcare11162295.
21- M. C. Thomas and S. P. Arjunan, "Deep Learning Measurement Model to Segment the Nuchal Translucency Region for the Early Identification of Down Syndrome," Measurement Science Review, vol. 22, no. 4, pp. 187–192, Aug. (2022), doi: 10.2478/msr-2022-0023.
22- N. Paredes, E. Caicedo-Bravo, and B. Bacca, "Emotion Recognition in Individuals with Down Syndrome: A Convolutional Neural Network-Based Algorithm Proposal," Symmetry (Basel), vol. 15, no. 7, Jul. (2023), doi: 10.3390/sym15071435.
23- B. Qin, L. Liang, J. Wu, Q. Quan, Z. Wang, and D. Li, "Automatic identification of down syndrome using facial images with deep convolutional neural network," Diagnostics, vol. 10, no. 7, Jul. (2020), doi: 10.3390/diagnostics10070487.
24- E. Setyati, S. Az, S. P. Hudiono, and F. Kurniawan, "CNN based Face Recognition System for Patients with Down and William Syndrome," Knowledge Engineering and Data Science, vol. 4, no. 2, p. 138, Dec. (2021), doi: 10.17977/um018v4i22021p138-144.
25- J. Wang and Y. Chen, "Introduction to Transfer Learning."
26- M. J. Bull, "Down Syndrome," New England Journal of Medicine, vol. 382, no. 24, pp. 2344–2352, Jun. (2020), doi: 10.1056/NEJMra1706537.
27- J. Langdon and H. Down, "Observations on an Ethnic Classification of Idiots," (1866).
28- A. J. Esbensen, Health conditions associated with aging and end of life of adults with down syndrome, vol. 39, no. C. (2010). doi: 10.1016/S0074-7750(10)39004-5.
29- A. Asim, A. Kumar, S. Muthuswamy, S. Jain, and S. Agarwal, "‘down syndrome: An insight of the disease,’" Journal of Biomedical Science, vol. 22, no. 1. BioMed Central Ltd., Jun. 11, (2015). doi: 10.1186/s12929-015-0138-y.
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31- Q. Zhao et al., "LNCS 8150 - Hierarchical Constrained Local Model Using ICA and Its Application to Down Syndrome Detection," (2013).
32- Q. Zhao et al., "Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA," Med Image Anal, vol. 18, no. 5, pp. 699–710, (2014), doi: 10.1016/j.media.2014.04.002.
33- F. F. Alkhalid, A. Q. Albayati, and A. A. Alhammad, "Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization," International Journal of Electrical and Computer Engineering, vol. 12, no. 2, pp. 1904–1909, Apr. (2022), doi: 10.11591/ijece.v12i2.pp1904-1909.
34- L. Perez and J. Wang, "The Effectiveness of Data Augmentation in Image Classification using Deep Learning," Dec. (2017), [Online]. Available: http://arxiv.org/abs/1712.04621
35- F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," Oct. (2016), [Online]. Available: http://arxiv.org/abs/1610.02357
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How to Cite
1.
Flayyeh Alkhalid F. Using Transfer Learning Approach for Down Syndrome Features Extraction and Data Augmentation for Data Expansion. Frontiers Biomed Technol. 2024;.
