Comparative Study of Deep Learning-Based Frameworks for Colorectal Cancer Detection Using Histopathological Images - Durgabhavani, Boddupelli.; Shaik , Amjan (Journal of Neonatal Surgery, 2025)
This paper compares three deep learning-based frameworks—LbCCD, ColoSeqNet, and ColoTL-Framework—for automatic colorectal cancer detection using histopathological images. Each framework builds upon CNN-based architectures with different enhancements: LbCCD uses Enhanced ResNet50, ColoSeqNet integrates LSTM for sequence modeling, and ColoTL-Framework employs transfer learning with fine-tuned VGG19. We evaluate these models on the Colorectal Histopathology dataset across multiple metrics. Our results show that ColoTL-Framework achieves the highest accuracy of 98.79%, followed by ColoSeqNet with 96.89%, and LbCCD with 93.40%. This study highlights the proposed methods' architectural novelties, performance improvements, and clinical relevance...