“You can think of it as a magnifying glass and a complete view of the scan at the same time,” Nasir explains.
The model was trained using CT scans of both healthy individuals and cancer patients and learned to recognize patterns that distinguish between normal, benign, and malignant cases.
The results show clear performance improvements. The system achieved over 96% accuracy, outperforming existing approaches and maintaining stable performance across a variety of tests. “This level of progress is important, especially in medical applications where small differences can have significant consequences,” says the KTU PhD student.
Applicable to more than just lung cancer – including brain tumors and breast cancer
In clinical practice, this system could change the way lung cancer is diagnosed.
“This is meant to support clinicians. The system provides a second opinion, helps ensure that important details are not overlooked, and reduces the time required per patient, especially in high-load environments”, emphasizes the KTU researchers.
For patients, the impact is even more significant. Lung cancer is often detected late because treatment options are limited. Early detection dramatically improves survival rates. “Early diagnosis means treatment can start sooner, and outcomes are generally much better,” Nasir says.
This system is designed to improve both sides of the problem. This means fewer cases are missed, while also reducing the number of false alarms that lead to unnecessary stress and procedures.
However, the researchers note that the current model was trained on a relatively limited dataset and needs to be tested on a larger and more diverse group of patients. “In a real-world situation, there are many variables, such as different scanners, imaging protocols, and patient populations, so we need to make sure the system works reliably for all of them,” Nasir explains.
Future steps include clinical validation, testing in hospital settings, and integration into existing healthcare systems.
In the future, the same approach may be applied beyond lung cancer. “Medical imaging tasks that require both detailed analysis and big-picture understanding could benefit from this type of model,” Nasir said, pointing to areas such as brain tumors, breast cancer, and eye diseases.
reference: Yousafzai SN, Nasir IM, Mansour S, et al. A hybrid deep learning approach integrating CNN and transformers for lung cancer classification using CT scans. science officer. 2026.doi: 10.1038/s41598-026-41161-7
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