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Deep-learning feature extraction with their subsequent selection and support vector machine classification of the breast ultrasound images
А.А. Kolchev 1, D.V. Pasynkov 2,3, I.A. Egoshin 2, I.V. Kliouchkin 4, О.О. Pasynkova 2

1 Kazan (Volga region) Federal University, Ministry of Education and Science of Russian Federation,
420008, Kazan, Russia, Kremlevskaya St. 18;
2 Mari State University, Ministry of Education and Science of Russian Federation,
424000, Yoshkar-Ola, Russia, Lenin square 1;
3 Kazan State Medical Academy - Branch Campus of the Federal State Budgetary Educational Institution
of Further Professional Education «Russian Medical Academy of Continuous Professional Education»,
Ministry of Healthcare of the Russian Federation, 420012, Kazan, Russia, Butlerova St 36;
4 Kazan Medical University, Ministry of Health of Russian Federation,
420012, Kazan, Russia, Butlerova St. 49

 PDF, 1641 kB

DOI: 10.18287/2412-6179-CO-1421

Pages: 753-761.

Full text of article: English language.

Abstract:
Our study aimed to develop a comprehensive system for discriminating between benign and malignant breast lesions on ultrasound images. The system integrated deep learning (DL) and conventional machine learning techniques. Our database consisted of 494 ultrasound images, comprising 231 benign and 263 malignant breast lesions. In the initial stage, we evaluated the performance of non-modified DL networks, including VGG-16, ResNet-18, and InceptionRes-NetV2. We assessed the results for the entire lesion as well as its inner and outer parts. For training the networks, we employed supervised transfer learning. In the second stage, we utilized a support vector machine (SVM) model for lesion classification. The features obtained from the modified DL networks, where we removed the last layers, were used for training and testing the SVM. In the final stage, we assessed the classification results using SVM, with a focus on selecting the most significant features obtained from the modified DL networks. We employed techniques such as ReliefF, FSCNCA, and LASSO for feature selection. Our three-step approach yielded impressive results, with an accuracy of 0.987, sensitivity of 0.989, and specificity of 0.983. These results significantly outperformed using only DL or DL+SVM without feature selection. Overall, our algorithm demonstrated sufficient accuracy in the clinical task of discriminating between benign and malignant breast lesions on ultrasound images.

Keywords:
ultrasound image, lesion, deep learning, SVM, feature extraction.

Citation:
Kolchev AA, Pasynkov DV, Egoshin IA, Kliouchkin IV, Pasynkova OO. Deep-learning feature extraction with their subsequent selection and support vector machine classification of the breast ultrasound images. Computer Optics 2024; 48(5): 753-761. DOI: 10.18287/2412-6179-CO-1421.

Acknowledgements:
The main results of sections "Materials and methods" and "Results" were obtained by D.V. Pasynkov and I.A. Egoshin with the support by Grant of Russian Science Foundation (Project 22-71-10070, https://rscf.ru/en/project/22-71-10070/). The authors are grateful to the Kazan Federal University Strategic Academic Leadership Program (PRIORITY-2030) for the technical feasibility of using hardware and software.

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