Histopathological Identification of Lung and Colon Combine Cancer using Deep Learning Approach
Sheshang Degadwala, Priya R. Oza · International Journal of Scientific Research in Science and Technology · 2025
Accurate histopathological differentiation of lung and colon malignancies is essential for timely clinical intervention, yet visual overlapping among tissue patterns often hinders manual diagnosis. This study presents a deep-learning framework that leverages a fine-tuned VGG-16 convolutional neural network to classify combined lung and colon cancer images from the publicly available LC25000 dataset. The dataset comprises 25,000 hematoxylin-and-eosin–stained micrographs evenly distributed among benign and malignant categories for both organs. ImageNet pre-trained weights were transferred, and the terminal convolutional blocks were unfrozen for domain-specific training while extensive rotation, flipping, and color-jitter augmentation mitigated class imbalance and overfitting. Following Adam optimization with an initial learning rate of 1 × 10-4, the model converged in 30 epochs, achieving 95 % overall accuracy, 0.94 F1-score, and 0.96 area under the receiver-operating characteristic curve on a held-out test set. Saliency and Grad-CAM visualizations indicated that discriminative regions corresponded to nuclear pleomorphism and glandular architecture, thereby providing interpretability consistent with pathological criteria. The experimental outcomes demonstrate that transfer learning with VGG-16 yields a fast, resource-efficient, and highly reliable computer-aided diagnostic tool for large-scale screening of lung and colon histopathology slides. Future enhancements may incorporate transformer-based attention and ensemble strategies to further refine performance across heterogeneous staining protocols.