Arif Barlas Çölgeçen
Class of 2026Istanbul, Marmara Region
About
Hi! My name is Arif Barlas Çölgeçen. I have a strong interest in artificial intelligence applications within healthcare, focusing on image processing. Motivated by the high mortality rate of lung cancer and the potential of AI to enhance early detection, I developed a project utilizing deep learning techniques to accurately classify various subtypes of lung cancer. Having completed this project, I aim to disseminate the findings to the broader medical and technological communities and explore collaborations to validate and implement AI-driven diagnostic tools in clinical settings.Projects
- AI-Based Early Lung Cancer Detection Using CT Scans With Multiple Classification with mentor Krti (Feb. 14, 2025)
Project Portfolio
AI-Based Early Lung Cancer Detection Using CT Scans With Multiple Classification
Started July 31, 2024
Abstract or project description
Cancer presents profound challenges due to the lack of effective treatment. Lung cancer, one of the most hazardous cancer types, causes 1.8 million deaths annually. However, if lung cancer in individuals who have died had been detected early, many of them might still be alive; therefore, early detection holds significant potential to save numerous lives. Moreover, emergent nations with limited healthcare infrastructure and insufficient availability of qualified radiologists, the likelihood of early detection is severely compromised. This study proposes the early detection of lung cancer using Artificial Intelligence (AI). Specifically, it employs multiple classification to distinguish between different subtypes of cancerous CT scans (adenocarcinoma, large cell carcinoma, squamous cell carcinoma) and normal scans. The ResNet50 architecture, pre-trained on the ImageNet dataset, is utilized for feature extraction and classification. ResNet50, pre-trained on the ImageNet dataset, was selected for its efficient feature extraction capabilities and ability to mitigate vanishing gradient issues, making it well-suited for processing complex medical images. The study achieved a noteworthy accuracy of 88 percent on the test set. The results demonstrate that ResNet50 can detect and classify cancer subtypes with a precision comparable to that of a radiologist, offering a promising tool to enhance diagnostic workflows, particularly in resource-limited settings. However, further validation in clinical environments is necessary to ensure robustness and reliability.