Flexible Framework for Lung and Colon Cancer Automated Analysis Across Multiple Diagnosis Scenarios

Among humans, lung and colon cancers are regarded as primary contributors to mortality and morbidity. They may grow simultaneously in organs, having a harmful influence on the lives of people. If tumor is not diagnosed early, it is likely to spread to both of those organs. This research presents a flexible framework that employs lightweight Convolutional Neural Networks architecture for automating lung and colon cancer diagnosis in histological images across multiple diagnosis scenarios. The LC25000 dataset is commonly used for this task. It includes 25000 histopathological images belonging to 5 distinct classes, which are lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and benign colonic tissue. This work includes three diagnosis scenarios: (S1) evaluates lung or colon samples, (S2) distinguishes benign from malignant images, and (S3) classifies images into five categories from the LC25000 dataset. Across all the scenarios, the scored accuracy, recall, precision, F1-score, and AUC exceeded 0.9947, 0.9947, and 0.9995, respectively. This investigation with a lightweight Convolutional Neural Network containing only 1.612 million parameters is extremely efficient for automated lung and colon cancer diagnosis, outperforming several current methods. This method might help doctors provide more accurate diagnoses and improve patient outcomes.

@article{SAKLI2025,
  title     = {Flexible Framework for Lung and Colon Cancer Automated Analysis Across Multiple Diagnosis Scenarios},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Marwen SAKLI and Chaker ESSID and Bassem BEN SALAH and Hedi SAKLI},
  doi       = {10.14569/IJACSA.2025.0160258},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160258}
}