• Dr. D. Prathyusha Reddi
• K. VISHNU VARDHAN REDDY
• J. POOJITHA
• G. VIJAYA LAKSHMI
• K. GURU RAJA

Lung cancer is one of the leading causes of cancer-related deaths worldwide. Early detection of lung nodules significantly improves patient survival rates. This study proposes an automated lung nodule detection system using Convolutional Neural Networks (CNN) on X-ray images. The system integrates preprocessing, feature extraction, and classification using GoogLeNet, a deep CNN architecture. The model was trained on a publicly available Kaggle dataset comprising 5856 chest X-ray images categorized into Pneumonia and Normal. Performance metrics such as accuracy, sensitivity, specificity, and precision were used for evaluation. The proposed model demonstrates high diagnostic performance and potential for assisting radiologists in early lung cancer detection.

Lung Nodule, CNN, GoogLeNet, Medical Imaging, X-ray, Deep Learning

"Lung Nodule detection in X-ray images using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.m251-m254, April-2025, Available :http://www.jetir.org/papers/JETIR2504C33.pdf

"Lung Nodule detection in X-ray images using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppm251-m254, April-2025, Available at : http://www.jetir.org/papers/JETIR2504C33.pdf