Efficacies of the CNN Algorithm in Predicting Lung Cancer

Abstract

Lung cancer is the leading cause of cancer-related death in the 21st century, and is highly expected to remain so in the future ages. It is possible to diagnose and treat the lung cancer if the proper symptoms of the diseases are detected in the early phases. A convolutional neural network (CNN)-based machine learning model is a recently emerged network design that has been functionalizing since the last decade mainly to optimize the detection processes of the lung cancer in the pre-collected medical examinations and the closely associated lung cancer datasets. Through the CNN classifier, the lung cancer patients are majorly classified based on their symptoms while the Python programming script drives the detection schemes as a whole through the effective implementation of the entire model. In fact, the CNN model enables the medical practitioners and hospital professionals to build the sustainable prototype mechanisms for the effective treatment of the lung cancer via the computational intelligence without any negative impacts on the societal environment. As it can reduce the amount of wasted resources and the amount of work required to complete the manual diagnosis tasks, the lung cancer patients can get the real-time treatment in a cost-effective manner with the minimal effort and latency from any location at any time. In this article, we overviews the performances of the CNN model designed by us, and assess its accuracy regimes in detecting the cancerous and noncancerous Lung cells. We believe that our model could mark the future prototype medical diagnosis scheme for the efficient lung cancer cells investigations, their progressive growing stages, and the timely assessments of the medical treatments.