Articles
An Intelligent Hybrid Framework Integrating Enhanced Fuzzy Rough Set Theory and Stacked Autoencoder Networks for MRI-Based Breast Cancer Diagnosis
Downloads
Keywords
Breast Cancer Diagnosis
MRI Imaging
Fuzzy Rough Set Theory
Stacked Autoencoder
Article Usage Statistics
Live Metrics
Abstract Views
119
File Views
88
PDF
0
HTML
0
Other
88
Total
207
Article Usage Chart
Abstract
File Views
PDF
HTML
Other
Total
Abstract
Breast cancer remains one of the most critical health challenges globally, necessitating accurate, early-stage diagnostic systems to improve survival rates. Magnetic Resonance Imaging (MRI) offers superior soft-tissue contrast, yet the complexity and high dimensionality of MRI data demand robust computational approaches. This study proposes an intelligent hybrid framework that integrates an enhanced fuzzy rough set (FRS) theory with stacked autoencoder (SAE) networks for efficient MRI-based breast cancer diagnosis. The framework addresses feature uncertainty, redundancy, and non-linearity through a two-stage process involving feature selection using improved fuzzy rough approximations and deep representation learning via stacked autoencoders. The proposed model enhances classification accuracy, reduces computational complexity, and improves interpretability. Experimental evaluation demonstrates superior performance compared to traditional machine learning and deep learning approaches. The findings highlight the framework’s potential in clinical decision support systems, offering improved diagnostic precision and reduced false-positive rates.
Downloads
Download data is not yet available.
References
1. U. K. Acharya and S. Kumar, “Genetic algorithm based adaptive histogram equalization (GAAHE) technique for medical image enhancement,” Optik, vol. 230, Mar. 2021, doi: 10.1016/j.ijleo.2021.166273.
2. S. A. Alanazi et al., “Boosting breast cancer detection using convolutional neural network,” Journal of Healthcare Engineering, pp. 1–11, Apr. 2021, doi: 10.1155/2021/5528622.
3. H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro, and A. Liotta, “Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning,” Computer Methods and Programs in Biomedicine, vol. 223, Aug. 2022, doi: 10.1016/j.cmpb.2022.106951.
4. R. Beňačka, D. Szabóová, Z. Guľašová, Z. Hertelyová, and J. Radoňák, “Classic and new markers in diagnostics and classification of breast cancer,” Cancers, vol. 14, no. 21, Nov. 2022, doi: 10.3390/cancers14215444.
5. R. Biswas, S. Roy, and A. Biswas, “MRI and CT image indexing and retrieval using steerable pyramid transform and local neighborhood difference pattern,” International Journal of Computers and Applications, vol. 44, no. 11, pp. 1005–1014, Nov. 2022, doi: 10.1080/1206212X.2022.2092937.
6. S. Boumaraf, X. Liu, C. Ferkous, and X. Ma, “A new computer-aided diagnosis system with modified genetic feature selection for BI-RADS classification of breast masses in mammograms,” BioMed Research International, vol. 2020, pp. 1–17, May 2020, doi: 10.1155/2020/7695207.
7. N. Chouhan, A. Khan, J. Z. Shah, M. Hussnain, and M. W. Khan, “Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography,” Computers in Biology and Medicine, vol. 132, May 2021, doi: 10.1016/j.compbiomed.2021.104318.
8. M. I. Daoud, S. Abdel-Rahman, T. M. Bdair, M. S. Al-Najar, F. H. Al-Hawari, and R. Alazrai, “Breast tumor classification in ultrasound images using combined deep and handcrafted features,” Sensors, vol. 20, no. 23, Nov. 2020, doi: 10.3390/s20236838.
9. M. Desai and M. Shah, “An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and convolutional neural network (CNN),” Clinical eHealth, vol. 4, pp. 1–11, 2021, doi: 10.1016/j.ceh.2020.11.002.
10. M. O. Divya and E. R. Vimina, “Content based image retrieval with multi-channel LBP and colour features,” International Journal of Applied Pattern Recognition, vol. 6, no. 2, 2020, doi: 10.1504/IJAPR.2020.10033780.
11. B. V Divyashree and G. H. Kumar, “Breast cancer mass detection in mammograms using gray difference weight and MSER detector,” SN Computer Science, vol. 2, no. 2, Apr. 2021, doi: 10.1007/s42979-021-00452-8.
12. O. J. Egwom, M. Hassan, J. J. Tanimu, M. Hamada, and O. M. Ogar, “An LDA–SVM machine learning model for breast cancer classification,” BioMedInformatics, vol. 2, no. 3, pp. 345–358, Jun. 2022, doi: 10.3390/biomedinformatics2030022.
13. H. Fang, H. Fan, S. Lin, Z. Qing, and F. R. Sheykhahmad, “Automatic breast cancer detection based on optimized neural network using whale optimization algorithm,” International Journal of Imaging Systems and Technology, vol. 31, no. 1, pp. 425–438, Mar. 2021, doi: 10.1002/ima.22468.
14. M. Gravina, S. Marrone, M. Sansone, and C. Sansone, “DAE-CNN: Exploiting and disentangling contrast agent effects for breast lesions classification in DCE-MRI,” Pattern Recognition Letters, vol. 145, pp. 67–73, May 2021, doi: 10.1016/j.patrec.2021.01.023.
15. A. Hizukuri, R. Nakayama, M. Nara, M. Suzuki, and K. Namba, “Computer-aided diagnosis scheme for distinguishing between benign and malignant masses on breast DCE-MRI images using deep convolutional neural network with bayesian optimization,” Journal of Digital Imaging, vol. 34, no. 1, pp. 116–123, Feb. 2021, doi: 10.1007/s10278-020-00394-2.
16. A. M. Ibraheem, K. H. Rahouma, and H. F. A. Hamed, “Automatic MRI Breast tumor detection using discrete wavelet transform and support vector machines,” in 2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES), Oct. 2019, pp. 88–91, doi: 10.1109/NILES.2019.8909345.
17. A. Khamparia et al., “Diagnosis of breast cancer based on modern mammography using hybrid transfer learning,” Multidimensional Systems and Signal Processing, vol. 32, no. 2, pp. 747–765, Apr. 2021, doi: 10.1007/s11045-020-00756-7.
18. S. Khan, N. Islam, Z. Jan, I. Ud Din, and J. J. P. C. 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, Jul. 2019, doi: 10.1016/j.patrec.2019.03.022.
19. T. R. Mahesh, V. V. Kumar, V. Vivek, K. M. K. Raghunath, and G. S. Madhuri, “Early predictive model for breast cancer classification using blended ensemble learning,” International Journal of System Assurance Engineering and Management, Jun. 2022, doi: 10.1007/s13198-022-01696-0.
20. G. Murtaza, L. Shuib, A. W. A. Wahab, G. Mujtaba, and G. Raza, “Ensembled deep convolution neural network-based breast cancer classification with misclassification reduction algorithms,” Multimedia Tools and Applications, vol. 79, no. 25–26, pp. 18447–18479, Jul. 2020, doi: 10.1007/s11042-020-08692-1.
21. M. Pavithra, R. Rajmohan, T. A. Kumar, and R. Ramya, “Prediction and classification of breast cancer using discriminative learning models and techniques,” in Machine Vision Inspection Systems, Volume 2, Wiley, 2021, pp. 241–262.
22. D. A. Ragab, O. Attallah, M. Sharkas, J. Ren, and S. Marshall, “A framework for breast cancer classification using multi-DCNNs,” Computers in Biology and Medicine, vol. 131, Apr. 2021, doi: 10.1016/j.compbiomed.2021.104245.
23. T. Sadad, A. Munir, T. Saba, and A. Hussain, “Fuzzy C-means and region growing based classification of tumor from mammograms using hybrid texture feature,” Journal of Computational Science, vol. 29, pp. 34–45, Nov. 2018, doi: 10.1016/j.jocs.2018.09.015.
24. D. Sheth and M. L. Giger, “Artificial intelligence in the interpretation of breast cancer on MRI,” Journal of Magnetic Resonance Imaging, vol. 51, no. 5, pp. 1310–1324, May 2020, doi: 10.1002/jmri.26878.
25. Y. Shen et al., “An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization,” Medical Image Analysis, vol. 68, Feb. 2021, doi: 10.1016/j.media.2020.101908.
26. V. K. Singh et al., “Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network,” Expert Systems with Applications, vol. 139, Jan. 2020, doi: 10.1016/j.eswa.2019.112855.
27. Z. Y. Tan, S. N. Basah, H. Yazid, and M. J. A. Safar, “Performance analysis of Otsu thresholding for sign language segmentation,” Multimedia Tools and Applications, vol. 80, no. 14, pp. 21499–21520, Jun. 2021, doi: 10.1007/s11042-021-10688-4.
28. N. M. ud Din, R. A. Dar, M. Rasool, and A. Assad, “Breast cancer detection using deep learning: Datasets, methods, and challenges ahead,” Computers in Biology and Medicine, vol. 149, Oct. 2022, doi: 10.1016/j.compbiomed.2022.106073.
29. A. H. Yurttakal, H. Erbay, T. İkizceli, and S. Karaçavuş, “Detection of breast cancer via deep convolution neural networks using MRI images,” Multimedia Tools and Applications, vol. 79, no. 21–22, pp. 15555–15573, Jun. 2020, doi: 10.1007/s11042-019-7479-6.
30. M. Yu, T. Quan, Q. Peng, X. Yu, and L. Liu, “A model-based collaborate filtering algorithm based on stacked AutoEncoder,” Neural Computing and Applications, vol. 34, no. 4, pp. 2503–2511, Feb. 2022, doi: 10.1007/s00521-021-05933-8.