SURVEY ON LUNG CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORKS
Chennasamudram Harsha, Manoj Krishna D, Akash P Keladi, Ananthsai Raghava K, M. Vinoth Kumar · International journal of advance research and innovative ideas in education · 2021
The deaths caused by cancer are increasing day by day one of the major reasons is lung cancer. Detecting lung cancer at the initial stages greatly lessens the number of patients who die and dramatically increases the likelihood that the patient will be saved. This paper aims to classify malignant and non-malignant development in the lung using the Convolutional Neural Network (CNN) algorithm. As a progressively mechanized methodology, the CNN technique uses picture information as input information and can be straightforwardly classified as yield. The machine will detect the image of the lung nodule participant in characteristics with various targets and dimensions when observing the disruption in the standard representation of the lung nodule due to its radiological complexity and fluctuation of sizes and shapes, thereby doing the constructive side of the classification function and enhancing the precision of classification steps. Many different methods of detecting lung cancer nodules exist but we will be focusing on Convolutional neural networks that utilize deep learning techniques.