Lung Cancer Disease Diagnosis Using Machine Learning Approach
Swati Mukherjee, Sneha Bohra
Abstract
The analysis and study of lung diseases has been the most intriguing investigation zone of medical experts from early days to the present day. To address this concern, a diagnosis system like this can only help diminish the odds of getting risk to human live by early discovery of malignant growth. By and by a couple of structures are proposed and still an enormous number of them are still a hypothetical plan. In the ensuing philosophy, the performance of a neural network model is examined to address this issue of recognizing cancerous cells in image data, an average issue in therapeutic imaging applications. In an attempt to accomplish this task, a lung cancer identification framework is developed based on AI and deep neural system, wherein the methodology depends on supervised learning for which a better precision has been obtained, especially by using the deep learning mechanism. CNN classification is a game plan of lung tumor classification. The framework includes various methods, for instance, picture acquisition, pre-preparing, enhancement, segmentation, feature extraction, and neural framework identification. To put it concisely, machine learning approach can give an unprecedented opportunity to improve decision support in lung cancer treatment at low cost.