Enhanced Colon Cancer Prediction Using Capsule Networks and Autoencoder-Based Feature Selection in Histopathological Images

The malignant development of cells in the colon or rectum is known as colon cancer, and because of its high incidence and possibility for death, it is a serious health problem. Because the disease frequently advances without symptoms in its early stages, early identification is essential. Improved survival rates and more successful therapy depend on an early and accurate diagnosis. The reliability of early detection can be impacted by problems with traditional diagnostic procedures, such as high false-positive rates, insufficient sensitivity, and inconsistent outcomes. This unique approach to colon cancer diagnosis uses autoencoder-based feature selection, capsule networks (CapsNets), and histopathology images to overcome these problems. CapsNets capture spatial hierarchies in visual input, improving pattern identification and classification accuracy. When employed for feature extraction, autoencoders reduce dimensionality, highlight important features, and eliminate noise, all of which enhance model performance. The suggested approach produced remarkable outcomes, with a 99.2% accuracy rate. The model's strong capacity to detect cancerous lesions with few mistakes is demonstrated by its high accuracy in differentiating between malignant and non-malignant tissues. This study represents a substantial development in cancer detection technology by merging autoencoders with Capsule Networks, so overcoming the shortcomings of existing approaches and offering a more dependable tool for early diagnosis. This method may improve patient outcomes, provide more individualized treatment regimens, and boost diagnostic accuracy.

@article{Ramesh2025,
  title     = {Enhanced Colon Cancer Prediction Using Capsule Networks and Autoencoder-Based Feature Selection in Histopathological Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Janjhyam Venkata Naga Ramesh and F. Sheeja Mary and S. Balaji and Divya Nimma and Elangovan Muniyandy and A. Smitha Kranthi and Yousef A. Baker El-Ebiary},
  doi       = {10.14569/IJACSA.2025.0160282},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160282}
}