Integration of Genetic Algorithm and Convolutional Neural Networks for Histopathological Image Analysis in Breast Cancer Diagnosis

Authors

  • Rashmi Gudur Dept. of Oncology,Krishna Vishwa Vidyapeeth, Karad, Maharashtra, India
  • Asif Ibrahim Tamboli Assistant Professor Department ofRadioiagnosis Krishna Vishwa Vidyapeeth, Karad, Maharashtra, India
  • Indrajeet Kumar Department of Computer Science and Engineering, Graphic Era Hill University Dehradun, Uttarakhand, India,
  • Kireet Joshi Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, 248002

Keywords:

Breast cancer diagnosis, Genetic Algorithm, CNN, Deep learning, Disease diagnosis

Abstract

Histopathological images frequently have complex patterns and structures that are difficult for conventional image processing methods to effectively analyse. In image classification applications, CNNs have demonstrated considerable potential, but their performance can be greatly influenced by the choice of the best hyperparameters, such as the depth of the architecture and the kind of filters to use. In this study, genetic algorithms are used to automatically find these ideal hyperparameters, enhancing the CNN's capability to detect breast cancer. The proposed hybrid model optimises the CNN architecture by utilising the evolutionary search capabilities of GAs, allowing it to successfully extract pertinent features and patterns from histopathology pictures. A CNN that is better suited for breast cancer categorization is produced by this dynamic optimisation process, increasing diagnostic precision. On a sizable collection of histopathology imaging data, rigorous tests were carried out to assess the efficacy of our method. Comparing the results to conventional CNN models, the findings show a considerable improvement in diagnosis accuracy. Additionally, the model is easier to use and more effective because to the incorporation of GAs, which also minimises the need for manual hyperparameter adjustment. In conclusion, a promising method for enhancing breast cancer diagnosis using histopathological image analysis is the combination of genetic algorithms with convolutional neural networks. This hybrid model's automated hyperparameter optimisation procedure offers precise and effective diagnostic abilities, ultimately improving patient outcomes in the area of breast cancer diagnosis and treatment.

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References

M. A. Molina-Cabello, J. A. Rodríguez-Rodríguez, K. Thurnhofer-Hemsi and E. López-Rubio, "Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms," 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9534085.

M. J. McAuliffe, F. M. Lalonde, D. McGarry, W. Gandler, K. Csaky and B. L. Trus, "Medical image processing analysis and visualization in clinical research", Computer-Based Medical Systems 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on, pp. 381-386, 2001.

P. Baran, S. Mayo, M. McCormack, S. Pacile, G. Tromba, C. Dullin, et al., "High-resolution X-ray phase-contrast 3-d imaging of breast tissue specimens as a possible adjunct to histopathology", IEEE Transactions on Medical Imaging, vol. 37, no. 12, pp. 2642-2650, 2018.

F. A. Spanhol, L. S. Oliveira, C. Petitjean and L. Heutte, "A dataset for breast cancer histopathological image classification", IEEE Transactions on Biomedical Engineering, vol. 63, no. 7, pp. 1455-1462, 2016.

S. Guan and M. Loew, "Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks", Journal of Medical Imaging, vol. 6, no. 3, 2019.

M. El Adoui, S. Mahmoudi, M. Larhmam and M. Benjelloun, "MRI breast tumor segmentation using different encoder and decoder CNN architectures", Computers, vol. 8, no. 3, 2019.

S. Khan, N. Islam, Z. Jan, I. Ud Din and J. Rodrigues, "A novel deep learning based framework for the detection and classification of breast cancer using transfer learning", Pattern Recognition Letters, vol. 125, pp. 1-6, 2019.

K. Spuhler, J. Ding, C. Liu, J. Sun, M. Serrano-Sosa, M. Moriarty, et al., "Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis", Magnetic Resonance in Medicine, vol. 82, no. 2, pp. 786-795, 2019.

F. Khameneh, S. Razavi and M. Kamasak, "Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network", Computers in Biology and Medicine, vol. 110, pp. 164-174, 2019.

N. Wahab, A. Khan and Y. Lee, "Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images", Microscopy, vol. 68, no. 3, pp. 216-233, 2019.

S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.

Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.

Borkar, P., Wankhede, V.A., Mane, D.T. et al. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08615-w

D. C. Cireşan, A. Giusti, L. M. Gambardella and J. Schmidhuber, "Mitosis detection in breast cancer histology images with deep neural networks", Med Image ComputComput Assist Interv, vol. 8150, pp. 411-418, 2013.

A. Cruz-Roa, A. Basavanhally, F. A. González, H. Gilmore, M. Feldman, S. Ganesan, et al., "Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks", Medical Imaging, 2014.

M. A. Molina-Cabello, E. López-Rubio, R. M. Luque-Baena, M. J. Rodríguez-Espinosa and K. Thurnhofer-Hemsi, "Blood cell classification using the hough transform and convolutional neural networks", World Conference on Information Systems and Technologies, pp. 669-678, 2018.

M. A. Molina-Cabello, C. Accino, E. López-Rubio and K. Thurnhofer-Hemsi, "Optimization of convolutional neural network ensemble classifiers by genetic algorithms", International Work-Conference on Artificial Neural Networks, pp. 163-173, 2019.

D. Abdelhafiz, C. Yang, R. Ammar and S. Nabavi, "Deep convolutional neural networks for mammography: Advances challenges and applications", BMC Bioinformatics, vol. 20, 2019

Chou S, Lee T, Shao YE, Chen I. Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert SystAppl 2004;27(1):133–42.

A. Chon, N. Balachandra, P. Lu, Deep convolutional neural networks for lung cancer detection, Standford University.

Cruz-Roa AA, Ovalle JEA, Madabhushi A, Osorio FAG. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: Medical image computing and computer-assisted intervention - (MICCAI) 2013 - 16th international conference, nagoya, Japan, september 22-26, 2013, proceedings, Part II; 2013. p. 403–10.

Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, Gonzalez FA, Larsen ABL, Vestergaard JS, Dahl AB, Ciresan DC, Schmidhuber J, Giusti A, Gambardella LM, Tek FB, Walter T, Wang C, Kondo S, Matuszewski BJ, Precioso F, Snell V, Kittler J, de Campos TE, Khan AM, Rajpoot NM, Arkoumani E, Lacle MM, Viergever MA, Pluim JPW. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal 2015;20(1):

Kumar R, Srivastava R, Srivastava S. Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features. Journal of medical engineering 2015.

Xing F, Xie Y, Yang L. An automatic learning-based framework for robust nucleus segmentation. IEEE Trans Med Imaging 2016;35(2):550–66.

Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Medical image computing and computer-assisted intervention - (MICCAI) 2013 - 16th international conference, nagoya, Japan, september 22-26, 2013, proceedings, Part II; 2013. p. 246–53.

Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08613-y

A. Mohamed, S. Fakhry and T. Basha, "Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 1440-1443, doi: 10.1109/EMBC48229.2022.9872011.

R. J. Santen et al., "Critical assessment of new risk factors for breast cancer: Considerations for development of an improved risk prediction model" in Endocrine-Related Cancer, Society for Endocrinology, vol. 14, no. 2, pp. 169-187, 2007.

K. Dembrower et al., "Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction", Radiology, vol. 294, no. 2, pp. 265-272, 2020.

Christopher Davies, Matthew Martinez, Catalina Fernández, Ana Flores, Anders Pedersen. Using Machine Learning for Early Detection of Learning Disabilities. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/172

Jenifa Sabeena, S. ., & Antelin Vijila, S. . (2023). Moulded RSA and DES (MRDES) Algorithm for Data Security. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 154–162. https://doi.org/10.17762/ijritcc.v11i2.6140

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Published

04.11.2023

How to Cite

Gudur, R. ., Tamboli, A. I. ., Kumar, I. ., & Joshi , K. . (2023). Integration of Genetic Algorithm and Convolutional Neural Networks for Histopathological Image Analysis in Breast Cancer Diagnosis. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 542–552. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3734

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Research Article

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