Exploiting Silhouette Principle Component For Dimension Reduction In Breast Ultrasound Images Classification

(1) * Etika Kartikadarma Mail (Fakultas Ilmu Komputer Universitas Dian Nuswantoro Semarang, Indonesia)
(2) Ahmad Zainul Fanani Mail (Fakultas Ilmu Komputer Universitas Dian Nuswantoro Semarang, Indonesia)
(3) Pujiono Pujiono Mail (Fakultas Ilmu Komputer Universitas Dian Nuswantoro Semarang, Indonesia)
(4) Affandy Affandy Mail (Fakultas Ilmu Komputer Universitas Dian Nuswantoro Semarang, Indonesia)
(5) Sari Ayu Wulandari Mail (Fakultas Teknik Universitas Dian Nuswantoro Semarang, Indonesia)
*corresponding author


This paper proposes the use of the Dimensional Reduction method with the Silhouette Principle Component (SPC) Approach to improve the classification of breast ultrasound images. The PCA method is used to reduce the dimensions of medical images to improve classification, with MobileNet-v2 and DenseNet-121 as the optimal classification algorithm choices. The results show that the SPC method succeeded in producing efficient feature representation with data sizes that are almost the same as the original data, while PCA produces greater dimensionality reduction. The SPC model also shows the best performance in terms of accuracy and loss. This research makes a significant contribution to the development of more sophisticated and efficient medical image analysis techniques to support the diagnosis and treatment of breast cancer.






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Hossein Talebi and Peyman Milanfar. 2021. Learning to Resize Images for Computer Vision Tasks. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.2103.09950

Quintana, G.I.; Li, Z.; Vancamberg, L.; Mougeot, M.; Desolneux, A.; Muller, S. Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification. Bioengineering 2023, 10, 534. https://doi.org/10.3390/bioengineering10050534

Basak, H., Kundu, R., Chakraborty, S. et al. Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection. SN COMPUT. SCI. 2, 369 (2021). https://doi.org/10.1007/s42979-021-00741-2

Salifu Nanga, Ahmed Tijani Bawah, Benjamin Ansah Acquaye, Mac- Issaka Billa, Francis Delali Baeta, Nii Afotey Odai, Samuel Kwaku Obeng, and Ampem Darko Nsiah. Review of dimension reduction meth- ods. Journal of Data Analysis and Information Processing , 9(3):189– 231, 2021.

Zhiyang Jin, Guorui Feng, Yanli Ren, and Xinpeng Zhang. Features extraction optimization of jpeg steganalysis based on residual images. Signals processing , 170:107455, 2020.

Xiaolu Han, Yun Liu, Zhenjiang Zhang, Xin L u ¨ , and Which Li. Sparse auto-encoder combined with kernel for network attack detection. Com puter Communications , 173:14–20, 2021.

Bartenhagen, C., Klein, H.U., Ruckert, C. et al. Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data. BMC Bioinformatics 11, 567 (2010). https://doi.org/10.1186/1471-2105-11-567

K. Koonsanit, D. Hiruma and N. Nishiuchi, "Dimension Reduction Method by Principal Component Analysis in the Prediction of Final User Satisfaction," 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), Kanazawa, Japan, 2022, pp . 649-650, doi: 10.1109/IIAIAAI55812.2022.00128.

C. Yumeng and F. Yinglan, "Research on PCA Data Dimension Reduction Algorithm Based on Entropy Weight Method," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020, pp. 392-396, doi: 10.1109/MLBDBI51377.2020.00084.

T. Zhang and B. Yang, "Big Data Dimension Reduction Using PCA," 2016 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA, 2016, pp. 152-157, doi: 10.1109/SmartCloud.2016.33.

Shutaywi M, Kachouie NN. Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering. Entropy (Basel). 2021 Jun 16;23(6):759. doi: 10.3390/e23060759. PMID: 34208552; PMCID: PMC8234541.

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863

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