ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection





breast cancer, ResNet50, bat algorithm, Extreme learning Machine, ultrasound image


INTRODUCTION: Breast cancer is one of the most common malignant tumors in women, and the incidence rate is increasing year by year. Women in every country in the world may develop breast cancer at any age after puberty. The cause of breast cancer is not fully understood. At present, the main methods of breast cancer detection are inefficient. Researchers are trying to use computer technology to detect breast cancer. But there are some still limitations.

METHODS: We propose a network (ODET) to detect breast cancer based on ultrasound images. In this paper, we use ResNet50 as the backbone model. We make some modifications to the backbone model by deep ELM-based transfer learning. After these modifications, the network is named DET. However, DET still has some shortcomings because the parameters in DET are randomly assigned and will not change in the experiment. In this case, we select BA to optimize DET. The optimized DET is named ODET.

RESULTS: The proposed ODET gets the F1-score (F1), precision (PRE), specificity (SPE), sensitivity (SEN), and accuracy (ACC) are 93.16%±1.12%, 93.28%±1.36%, 98.63%±0.31%, 93.96%±1.85%, and 97.84%±0.37%, respectively.

CONCLUSION: It proves that the proposed ODET is an effective method for breast cancer detection.


Journal article: F. Gao et al., "SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis," Computerized Medical Imaging and Graphics, vol. 70, pp. 53-62, 2018.

Journal article: Z. Wang et al., "Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features," IEEE Access, vol. 7, pp. 105146-105158, 2019.

Journal article: R. Rouhi, M. Jafari, S. Kasaei, and P. Keshavarzian, "Benign and malignant breast tumors classification based on region growing and CNN segmentation," Expert Systems with Applications, vol. 42, no. 3, pp. 990-1002, 2015.

Journal article: J. Zuluaga-Gomez, Z. Al Masry, K. Benaggoune, S. Meraghni, and N. Zerhouni, "A CNN-based methodology for breast cancer diagnosis using thermal images," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 9, no. 2, pp. 131-145, 2021.

Journal article: T. Mahmood, M. Arsalan, M. Owais, M. B. Lee, and K. R. Park, "Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs," Journal of clinical medicine, vol. 9, no. 3, p. 749, 2020.

Journal article: K. Gupta and N. Chawla, "Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pre-trained CNN," Procedia Computer Science, vol. 167, pp. 878-889, 2020.

Journal article: B. Ay, C. Turker, E. Emre, K. Ay, and G. Aydin, "Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features," Computers in Biology and Medicine, vol. 147, p. 105725, 2022.

Journal article: Y. Zhang, W. Liu, X. Wang, and H. Gu, "A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN," Renewable Energy, 2022.

Conference: J. Fang, H. Lin, X. Chen, and K. Zeng, "A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 1103-1112.

Journal article: A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.

Conference: C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9.

Journal article: K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.

Conference: G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.

Conference: Z. Qin, Z. Zhang, X. Chen, C. Wang, and Y. Peng, "Fd-mobilenet: Improved mobilenet with a fast downsampling strategy," in 2018 25th IEEE International Conference on Image Processing (ICIP), 2018: IEEE, pp. 1363-1367.

Conference: K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.

Journal article: G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.

Journal article: X.-S. Yang, "Bat algorithm for multi-objective optimisation," arXiv preprint arXiv:1203.6571, 2012.

Conference: P. Gavrikov and J. Keuper, "CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 19066-19076.

Journal article: Y. Shi, Z. Wang, X. Du, G. Ling, W. Jia, and Y. Lu, "Research on the membrane fouling diagnosis of MBR membrane module based on ECA-CNN," Journal of Environmental Chemical Engineering, vol. 10, no. 3, p. 107649, 2022.

Journal article: J. Miettinen et al., "Whitening CNN-Based Rotor System Fault Diagnosis Model Features," Applied Sciences, vol. 12, no. 9, p. 4411, 2022.

Conference: L. Shen, M. Ziaeefard, B. Meyer, W. Gross, and J. J. Clark, "Conjugate Adder Net (CAddNet)-A Space-Efficient Approximate CNN," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 2793-2797.

Journal article: X. Miao, Y. Zhang, J. Zhang, and X. Liang, "Hierarchical CNN Classification of Hyperspectral Images Based on 3D Attention Soft Augmentation," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022.

Journal article: M. M. Althobaiti et al., "Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images," BioMed Research International, vol. 2022, 2022.

Journal article: M. A. Ozdemir, D. H. Kisa, O. Guren, and A. Akan, "Hand gesture classification using time–frequency images and transfer learning based on CNN," Biomedical Signal Processing and Control, vol. 77, p. 103787, 2022.

Journal article: X. Hu, M. Hu, and X. Yang, "A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine," Applied Sciences, vol. 12, no. 7, p. 3388, 2022.

Journal article: A. Mohaghegh, S. Farzin, and M. V. Anaraki, "A new framework for missing data estimation and reconstruction based on the geographical input information, data mining, and multi-criteria decision-making; theory and application in missing groundwater data of Damghan Plain, Iran," Groundwater for Sustainable Development, vol. 17, p. 100767, 2022.

Journal article: G. Ahmed, T. Chu, and K. Loo, "Novel Multicolumn Kernel Extreme Learning Machine for Food Detection via Optimal Features from CNN," arXiv preprint arXiv:2205.07348, 2022.

Journal article: L. Boussaad and A. Boucetta, "Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors," International Journal of Applied Metaheuristic Computing (IJAMC), vol. 13, no. 1, pp. 1-18, 2022.

Conference: R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 618-626.

Conference: S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, "Cbam: Convolutional block attention module," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3-19.

Conference: L. Chen, J. Chen, H. Hajimirsadeghi, and G. Mori, "Adapting grad-cam for embedding networks," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 2794-2803.

Conference: A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian, "Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks," in 2018 IEEE winter conference on applications of computer vision (WACV), 2018: IEEE, pp. 839-847.

Journal article: Y. Zhang and L. Wu, "Improved image filter based on SPCNN," Science in China Series F: Information Sciences, vol. 51, no. 12, pp. 2115-2125, 2008.

Journal article: Y. Zhang and L. Wu, "Segment-based coding of color images," Science in China Series F: Information Sciences, vol. 52, no. 6, pp. 914-925, 2009.




How to Cite

Zhu Z, Wang S. ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection. EAI Endorsed Scal Inf Syst [Internet]. 2022 Sep. 29 [cited 2023 Dec. 11];10(2):e4. Available from: https://publications.eai.eu/index.php/sis/article/view/1747

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