International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Hybrid Optimized Deep Learning Techniques for Analyzing Mammograms

  • Bandaru, Satish Babu (Department of Computer Science and Engineering, Annamalai University) ;
  • Deivarajan, Natarajasivan (Department of Computer Science and Engineering, Faculty of Computer Science and Engineering, Annamalai University) ;
  • Gatram, Rama Mohan Babu (Department of Computer Science and Engineering (AI & ML), RVR & JC College of Engineering)
  • Received : 2022.10.05
  • Published : 2022.10.30
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Abstract

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

Keywords

References

  1. Malebary, S. J., & Hashmi, A. (2021). Automated breast mass classification system using deep learning and ensemble learning in digital mammogram. IEEE Access, 9, 55312-55328. https://doi.org/10.1109/ACCESS.2021.3071297
  2. Hamed, G., Marey, M. A. E. R., Amin, S. E. S., & Tolba, M. F. (2020, April). Deep learning in breast cancer detection and classification. In The International Conference on Artificial Intelligence and Computer Vision (pp. 322-333). Springer, Cham.
  3. Al-Antari, M. A., Al-Masni, M. A., & Kim, T. S. (2020). Deep learning computer-aided diagnosis for breast lesion in digital mammogram. Deep Learning in Medical Image Analysis, 59-72.
  4. El Houby, E. M., & Yassin, N. I. (2021). Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks. Biomedical Signal Processing and Control, 70, 102954. https://doi.org/10.1016/j.bspc.2021.102954
  5. Cai, H., Huang, Q., Rong, W., Song, Y., Li, J., Wang, J., ... & Li, L. (2019). Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Computational and mathematical methods in medicine, 2019.
  6. Jung, H., Kim, B., Lee, I., Yoo, M., Lee, J., Ham, S., ... & Kang, J. (2018). Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PloS one, 13(9), e0203355. https://doi.org/10.1371/journal.pone.0203355
  7. Agnes, S. A., Anitha, J., Pandian, S., & Peter, J. D. (2020). Classification of mammogram images using multiscale all convolutional neural network (MA-CNN). Journal of medical systems, 44(1), 1-9. https://doi.org/10.1007/s10916-019-1451-x
  8. Chouhan, N., Khan, A., Shah, J. Z., Hussnain, M., & Khan, M. W. (2021). Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography. Computers in Biology and Medicine, 132, 104318. https://doi.org/10.1016/j.compbiomed.2021.104318
  9. Touami, R., & Benamrane, N. (2021). Microcalcification Detection in Mammograms Using Particle Swarm Optimization and Probabilistic Neural Network. Computacion y Sistemas, 25(2), 369-379.
  10. Sangeetha, K., & Prakash, S. (2021). Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) with Harmony Search Optimization (HSO) for Early Breast Cancer Detection System. NeuroQuantology, 19(7), 1. https://doi.org/10.14704/nq.2021.19.7.NQ21079
  11. Cai, X., Li, X., Razmjooy, N., & Ghadimi, N. (2021). Breast Cancer Diagnosis by Convolutional Neural Network and Advanced Thermal Exchange Optimization Algorithm. Computational and Mathematical Methods in Medicine, 2021.
  12. Ittannavar, S. S., & Havaldar, R. H. (2022). Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm. BioMed Research International, 2022.
  13. Kanya Kumari, L., & Naga Jagadesh, B. (2022). An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection. International Journal of System Assurance Engineering and Management, 1-14.
  14. Gnanasekaran, V. S., Joypaul, S., Sundaram, P. M., & Chairman, D. D. (2020). Deep learning algorithm for breast masses classification in mammograms. IET Image Processing, 14(12), 2860-2868. https://doi.org/10.1049/iet-ipr.2020.0070
  15. Khan, H. N., Shahid, A. R., Raza, B., Dar, A. H., & Alquhayz, H. (2019). Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access, 7, 165724-165733. https://doi.org/10.1109/ACCESS.2019.2953318
  16. Chen, X., Xu, B., Yu, K., & Du, W. (2018). Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering. Journal of Applied Mathematics, 2018.
  17. Rao, R. V., & Patel, V. (2013). An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica, 20(3), 710-720.
  18. Ab Wahab, M. N., Nefti-Meziani, S., & Atyabi, A. (2015). A comprehensive review of swarm optimization algorithms. PLoS One, 10(5), e0122827. https://doi.org/10.1371/journal.pone.0122827
  19. Belhaj Soulami, K., Kaabouch, N., Saidi, M. N., & Tamtaoui, A. (2020). An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms. Multimedia Tools and Applications, 79(27), 18941-18979. https://doi.org/10.1007/s11042-019-08449-5
  20. Li, Z., Huang, J., Wang, J., & Ding, M. (2021). Development and application of hybrid teaching-learning genetic algorithm in fuel reloading optimization. Progress in Nuclear Energy, 139, 103856. https://doi.org/10.1016/j.pnucene.2021.103856
  21. Esfahanian, P., & Akhavan, M. (2019). Gacnn: Training deep convolutional neural networks with genetic algorithm. arXiv preprint arXiv:1909.13354.