• Jayda Deorankar
• Shivam patil
• Adarsh Veer

The analysis and study of lung diseases has been the most intriguing investigation zone of medical experts from early days to the present day. The lung cancer detection project is an innovative solution that utilizes advanced convolutional neural network (CNN) algorithms to enable early and accurate detection of lung cancer. By analyzing medical images, the project aims to address the challenges of late-stage diagnosis, misdiagnosis, and limited accessibility to screening facilities. The unique features of the project include real-time on-site screening using Raspberry Pi, automated image analysis, seamless integration with existing healthcare systems, and a user-friendly interface. The project holds promise in improving patient outcomes by facilitating timely interventions and personalized treatment plans. With its potential to enhance the efficiency, accuracy, and accessibility of lung cancer detection, the project has significant implications for the healthcare industry and the well-being of individuals at risk of lung cancer. To put it concisely, machine learning approach can give an unprecedented opportunity to improve decision support in lung cancer treatment at low cost.

CNN, image processing, lung cancer, raspberry pi

"Lung cancer detection using CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.n323-n327, May-2023, Available :http://www.jetir.org/papers/JETIR2305D49.pdf

"Lung cancer detection using CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppn323-n327, May-2023, Available at : http://www.jetir.org/papers/JETIR2305D49.pdf