Article
Authors: Aayad Nabeel, Mostafa Ragheb, Galina Momcheva, Issa Kamar, Mohamad Hamady
Artificial Intelligence: Methodology, Systems, and Applications: 19th International Conference, AIMSA 2024, Varna, Bulgaria, September 18–20, 2024, Proceedings
Pages 93 - 103
Published: 01 February 2025 Publication History
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Abstract
This study investigates the machine learning techniques for unsupervised image classification and quality assessment in the domain of ultrasound imaging. Leveraging Convolutional Neural Networks (CNNs) for feature extraction and subsequent integration into a Support Vector Machine (SVM) model, we explored a novel approach aimed at accurate image classification. The dataset comprises high-frequency images in the form of image sequences depicting the facial skin of females. The study's primary emphasis was to categorize ultrasound images based on learned deep features, offering a distinctive framework for unsupervised image classification. The investigation employed CNNs to extract deep features from images, enhancing the SVM model's performance in accurately categorizing images. The incorporation of gamma correction as a preprocessing step further augmented the accuracy and sensitivity of the models. The SVM model exhibited exceptional performance, achieving accuracy rates exceeding 95.43% in the training phase and approximately 94.72% during testing, that is a significant milestone in the precise classification of ultrasound images.
References
[1]
Mir, T.A., Banerjee, D., Upadhyay, D., Rawat, R.S.: Comprehensive facial acne classification: CNN-SVM synergy. In: 2nd World Conference on Communication and Computing (WCONF), RAIPUR, India, pp. 1–5 (2024)
[2]
Kothari, A., Shah, D., Soni, T., Dhage, S.: Cosmetic skin type classification using CNN with product recommendation. In: 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp. 1–6 (2021)
[3]
Carovac A, Smajlovic F, and Junuzovic D Application of ultrasound in medicine Acta Informatica Medica 2011 19 3 168
[4]
Antico, M., et al.: Deep learning for US image quality assessment based on femoral cartilage boundary detection in autonomous knee arthroscopy. IEEE Trans. Ultra. Ferroelec. Frequency Control 67(12), 2543–2552 (2020)
[5]
Vatiwutipong P, Vachmanus S, Noraset T, and Tuarob S Artificial intelligence in cosmetic dermatology: a systematic literature review IEEE Access 2023 11 71407-71425
[6]
Mishra, S., et al.: A comprehensive review on skin disease classification using convolutional neural network and support vector machine. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds.) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol. 1749. Springer, Cham (2023).
[7]
Zaid GH et al. Comparison between Convolutional Neural Network CNN and SVM in skin cancer images recognition J. Tech. 2021 3 4 15-22
[8]
Raza K and Singh NK A tour of unsupervised deep learning for medical image analysis Curr. Med. Imaging 2021 17 9 1059-1077
[9]
Wang G, Jiang C, Shen Z, Miao Y, and Wang H SFGAN: unsupervised generative adversarial learning of 3D scene flow from the 3D scene self Adv. Intell. Syst. 2022 4 4 2100197
[10]
Zhou, S., et al.: A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions. arXiv:2206.07579 (2022)
[11]
Xu, J.: A review of self-supervised learning methods in the field of medical image analysis. Int. J. Image Graph. Sig. Process. (IJIGSP) 13(4), 33–46 (2021)
[12]
Wei R and Mahmood A Recent advances in variational autoencoders with representation learning for biomedical informatics: a survey IEEE Access 2020 9 4939-4956
[13]
Nesovic, K., Koh, R.G., Sereshki, A.A., Zadeh, F.S., Popovic, M.R., Kumbhare, D.: Ultrasound Image Quality Evaluation using a Structural Similarity Based Autoencoder. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4002–4005 (2021)
[14]
Ghojogh, B., Crowley, M., Karray, F., Ghodsi, A.: Uniform Manifold Approximation and Projection (UMAP). In: Elements of Dimensionality Reduction and Manifold Learning, pp. 479–497 (2023)
[15]
Verma M, Srivastava M, Chack N, Diswar AK, and Gupta N A comparative study of various clustering algorithms in data mining Int. J. Eng. Res. Appl. (IJERA) 2012 2 3 1379-1384
[16]
Xu, H., et al.: A spectral clustering method combining path with density. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 695–698 (2012)
[17]
Kim, J.-C., Kim, M.-H., Suh, H.-E., Naseem, M.T., Lee, C.: Hybrid approach for facial expression recognition using convolutional neural networks and SVM. Appl. Sci. 12(5493), 12115493 (2022)
[18]
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Proceedings of the European conference on computer vision (ECCV), pp. 132–149 (2018)
[19]
Song, C., Liu, F., Huang, Y., Wang, L., Tan, T.: Auto-encoder based data clustering. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 18th Iberoamerican Congress, CIARP 2013, Havana, Cuba, 20–23 November, Proceedings, Part I vol. 18, pp. 117–124 (2013)
[20]
Soleymani, F., Eslami, M., Elze, T., Bischl, B., Rezaei, M.: Deep variational clustering framework for self-labeling large-scale medical images. Med. Imaging Image Process. 12032, 68–76) (2022)
[21]
Yu R et al. Feature discretization-based deep clustering for thyroid ultrasound image feature extraction Comput. Biol. Med. 2022 146 105600
Digital Library
[22]
Song Y et al. Medical ultrasound image quality assessment for autonomous robotic screening IEEE Robot. Autom. Lett. 2022 7 3 6290-6296
[23]
Czajkowska J, Juszczyk J, Piejko L, and Glenc-Ambroży M High-frequency ultrasound dataset for deep learning-based image quality assessment Sensors 2022 22 4 1478
Index Terms
Advanced CNN-SVM Machine Learning Techniques for Facial Skin Ultrasound Image Analysis
Computing methodologies
Artificial intelligence
Computer vision
Computer vision representations
Image representations
Computer graphics
Image manipulation
Image processing
Machine learning
Machine learning approaches
Neural networks
Security and privacy
Security services
Authentication
Biometrics
Index terms have been assigned to the content through auto-classification.
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Information
Published In
Artificial Intelligence: Methodology, Systems, and Applications: 19th International Conference, AIMSA 2024, Varna, Bulgaria, September 18–20, 2024, Proceedings
Sep 2024
248 pages
ISBN:978-3-031-81541-6
DOI:10.1007/978-3-031-81542-3
- Editors:
- Petia Koprinkova-Hristova
https://ror.org/05fpsjc82Institute of Information and Communication Technologies (IICT), Bulgarian Academy of Sciences, Sofia, Bulgaria
, - Nikola Kasabov
https://ror.org/01zvqw119Auckland University of Technology, Auckland, New Zealand
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 February 2025
Author Tags
- medical ultrasound
- deep learning
- facial skin images
- CNN-SVM
Qualifiers
- Article
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