Abstract: Globally, lung cancer remains the leading cause of cancer-related deaths, with early detection significantly improving survival rates. Developing robust machine learning models for early detection necessitates access to high-quality, localized datasets. This project establishes the first lung cancer dataset in Tunisia, utilizing DICOM CT scans from 123 Tunisian patients. The dataset, annotated by experienced radiologists, includes diverse forms of lung cancer at various stages. Using transfer learning with pre-trained 3D ResNet models from Tencent’s MedicalNet, our tests showed the dataset outperformed previous models in specificity and sensitivity. This demonstrates its effectiveness in capturing the unique clinical characteristics of the Tunisian population and its potential to significantly enhance lung cancer diagnosis and detection.

Omar Khouadja, Mohamed Saber Naceur, Samira Mhamedi and Anis Baffoun. “Tunisian Lung Cancer Dataset: Collection, Annotation and Validation with Transfer Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01507121

@article{Khouadja2024,
title = {Tunisian Lung Cancer Dataset: Collection, Annotation and Validation with Transfer Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01507121},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01507121},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Omar Khouadja and Mohamed Saber Naceur and Samira Mhamedi and Anis Baffoun}
}