MCNN-SVM: A Hybrid Deep Learning and SVM-Based Framework for Lung and Colon Cancer Image Classification - Khabiya, Priyanka; Parwej, Firoj (Journal of Neonatal Surgery, 2025)
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.