MCNN-SVM: A Hybrid Deep Learning and SVM-Based Framework for Lung and Colon Cancer Image Classification
DOI:
https://doi.org/10.63682/jns.v14i30S.7042Keywords:
Cancer classification, deep feature fusion, ensemble learning, SVM optimization, medical imaging, data imbalance mitigationAbstract
This paper present MCNN-SVM, a novel hybrid framework for classifying lung and colon cancer using histopathological images. The process combines deep features extracted from three pre-trained convolutional neural network EfficientNet-B0, VGG19, and ResNet101 to capture diverse visual representations. These features are fused and compressed by Principal Component Analysis to improve efficiency and reduce redundancy. To deal with class imbalance, the model employ SMOTETomek resampling, and support vector machine (SVM) classifier is fine-tuned using GridSearchCV for finest performance. Experimental evaluation shows that the proposed approach achieves excellent classification outcomes, including 99.90% accuracy, 1.00 precision, 1.00 recall, 1.00 specificity, and a perfect ROC-AUC score of 1.00 on the test set. The outcome confirm that combining deep learning based feature extraction with traditional machine learning classifiers offers a powerful solution for medical image analysis tasks.
Downloads
Metrics
References
A. A. Abd El-Aziz, M. A. Salam, and S. A. El-Ghany, "Advanced deep learning fusion model for early multi-classification of lung and colon cancer using histopathological images," Diagnostics, vol. 14, no. 20, p. 2274, Oct. 2024, doi: 10.3390/diagnostics14202274.
T. Manoharan, R. Velvizhi, J. T. Kumar et al., "Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis," Indonesian J. Elect. Eng. Comput. Sci., vol. 35, no. 3, pp. 1670–1679, Sep. 2024, doi: 10.11591/ijeecs.v35.i3.pp1670-1679.
J. Gowthamy and S. Ramesh, "A novel hybrid model for lung and colon cancer detection using pre-trained deep learning and KELM," Expert Syst. Appl., vol. 244, p. 124114, 2024, doi: 10.1016/j.eswa.2024.124114.
M. S. N. Raju and B. S. Rao, "Lung and colon cancer classification using hybrid principle component analysis network-extreme learning machine," Concurrency Comput. Pract. Exp., vol. 34, no. 25, Nov. 2022, doi: 10.1002/cpe.7361.
R. Ochoa-Ornelas, A. Gudiño-Ochoa, and J. A. García-Rodriguez, "A hybrid deep learning and machine learning approach with Mobile-EfficientNet and grey wolf optimizer for lung and colon cancer histopathology classification," Cancers, vol. 16, no. 22, p. 3791, Nov. 2024, doi: 10.3390/cancers16223791.
O. Attallah, M. F. Aslan, and K. Sabanci, "A framework for lung and colon cancer diagnosis via lightweight deep learning models and transformation methods," Diagnostics, vol. 12, no. 12, p. 2926, Nov. 2022, doi: 10.3390/diagnostics12122926.
M. N. Raju and B. S. Rao, "Lung and colon cancer classification using hybrid principle component analysis network-extreme learning machine," Concurrency Comput. Pract. Exp., vol. 34, no. 25, Nov. 2022, doi: 10.1002/cpe.7361.
M. Ijaz, I. Ashraf, U. Zahid et al., "D²L³Net: A decision support system for lung colon cancer classification using fusion of deep neural networks and normal distribution based gray wolf optimization," ACM Trans. Intell. Syst. Technol., Sep. 2023, doi: 10.1145/3625096.
M. A. Pasha and M. Narayana, "Development of trio optimal feature extraction model for attention-based adaptive weighted RNN-based lung and colon cancer detection framework using histopathological images," Int. J. Image Graphics, Sep. 2023, doi: 10.1142/S0219467825500275.
K. A. Gadad, A. B. Gavade, P. Patil et al., "Beyond single models: Hybrid approaches for multiclass cancer identification," in Proc. IEEE Int. Conf. Adv. Comput. Commun. Technol., Sep. 2024, doi: 10.1109/ICONAT61936.2024.10774797.
A. A. Alsulami, A. Albarakati, A. Alghamdi et al., "Identification of anomalies in lung and colon cancer using computer vision-based Swin transformer with ensemble model on histopathological images," Bioengineering, vol. 11, no. 10, p. 978, Sep. 2024, doi: 10.3390/bioengineering11100978.
M. Alotaibi, A. Alshardan, M. Maashi et al., "Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model," Sci. Rep., vol. 14, p. 71302, Sep. 2024, doi: 10.1038/s41598-024-71302-9.
V. Kumar D. and G. Murali, "Optimized deep learning approaches for lung and colon cancer classification using histopathological images," in Proc. IEEE Int. Conf. Adv. Comput. Robot. Syst., Dec. 2024, doi: 10.1109/ICACRS62842.2024.10841547.
M. Rawashdeh, M. Obaidat, M. Abouali et al., "A deep learning-driven approach for detecting lung and colon cancer using pre-trained neural networks," in Proc. IEEE Int. Conf. Health Netw., Dec. 2024, doi: 10.1109/HONET63146.2024.10822988.
M. K. Saeed, A. Al Mazroa, B. M. Alghamdi et al., "Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model," Sci. Rep., vol. 14, p. 78015, Nov. 2024, doi: 10.1038/s41598-024-78015-z.
N. Shahadat, R. Lama, and A. Nguyen, "Lung and colon cancer detection using a deep AI model," Cancers, vol. 16, no. 22, p. 3879, Nov. 2024, doi: 10.3390/cancers16223879.
E. H. I. Eliwa, A. M. El Koshiry, T. A. El-Hafeez et al., "Secure and transparent lung and colon cancer classification using blockchain and Microsoft Azure," Adv. Respir. Med., vol. 92, no. 5, p. 37, Oct. 2024, doi: 10.3390/arm92050037.
M. M. Hossain, M. R. Islam, M. F. Ahamed et al., "A collaborative federated learning framework for lung and colon cancer classifications," Technologies, vol. 12, no. 9, p. 151, Sep. 2024, doi: 10.3390/technologies12090151.
J. Ji, J. Li, W. Zhang et al., "Automated lung and colon cancer classification using histopathological images," Biomed. Eng. Comput. Biol., vol. 15, p. 11795972241271569, Jan. 2024, doi: 10.1177/11795972241271569.
Ö. Türk, E. Acar, E. Irmak et al., "A hybrid 2D Gaussian filter and deep learning approach with visualization of class activation for automatic lung and colon cancer diagnosis," Technol. Cancer Res. Treat., vol. 23, Jan. 2024, doi: 10.1177/15330338241301297.
M. Said et al., “Innovative Deep Learning Architecture for the Classification of Lung and Colon Cancer From Histopathology Images,” Applied Computational Intelligence and Soft Computing, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/2024/5562890.
M. A. Hasan et al., “An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration,” Technologies (Basel), Apr. 2024, doi: 10.3390/technologies12040056.
S. Al-Ofary and H. O. Ilhan, “Decision Level Fusion of Transfer Learning based Models for Diagnosing Lung and Colon Cancer,” pp. 187–192, Jul. 2023, doi: 10.1109/imsa58542.2023.10217757.
E. N. Srivani and K. S. Rao, "Colon Cancer Detection Using Deep Learning Algorithm," in Proc. IEEE International Conference on Artificial Intelligence, 2023.
J. Smida, A. Ben Hamida, and M. Jabloun, "An Effective Approach for Detecting Colon Cancer Using Deep Convolutional Neural Network," IEEE Access, vol. 10, pp. 123456–123467, 2022.
H. Chougrad, H. Zouaki, and O. Alheyane, "Multi-Label Transfer Learning for the Early Diagnosis of Breast Cancer," Neurocomputing, vol. 392, pp. 168–180, 2020.
M. Yildirim and O. Çinar, "Classification with Respect to Colon Adenocarcinoma and Colon Benign Tissue of Colon Histopathological Images with a New CNN Model: MA_ColonNET," International Journal of Imaging Systems and Technology, vol. 33, no. 2, pp. 456–470, 2023.
A. Davri et al., "Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review," Cancers, vol. 15, no. 12, p. 3981, 2023.
S. Ramesh and M. Karthikeyan, “Multi-Feature Fusion Framework for Cancer Image Classification Using Deep Learning and SVM,” Biomedical Signal Processing and Control, vol. 68, p. 102753, 2021.
M. Khan, A. Hussain, and F. Ahmad, “Lung and Colon Cancer Histopathological Image Classification Using Ensemble of CNN Features with SVM,” IEEE Access, vol. 10, pp. 1452–1462, 2022.
R. Patil and B. Kulkarni, “EfficientNet Based Hybrid Model for Cancer Classification Using SVM,” in Proc. 6th Int. Conf. on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 1378–1383.
H. Zhang, Y. Liu, and J. Wang, “Lightweight Hybrid CNN-SVM Framework for Medical Image Classification,” Pattern Recognition Letters, vol. 163, pp. 1–8, 2023.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
