An Efficient Lung Cancer Detection Model using Convnets and Residual Neural Networks

Lung cancer is a formidable global health challenge, representing a major cause of Mortalities. One of the main causes of cancer mortality globally, lung cancer demands innovative approaches to improve early detection, diagnosis, and subsequent treatment outcomes. Machine learning, a subset of artificial intelligence, provides a stating avenue to enhance the reliability and efficiency of lung cancer detection. The purpose of this work is to make effective use of the capabilities of deep learning to develop a robust and effective tool for the early detection of lung cancer through the analysis of CT scan images. We used a lung cancer dataset from Kaggle for experimentation. We applied two techniques namely CNN, Resnet50 for lung cancer detection and acquired reasonable detection rate. The results shown that proposed model acquired good accuracy when compared to conventional methods for lung cancer detection.