Analysis of Lung Cancer Detection Based on the Machine Learning Algorithm and IOT

Early detection of sickness can help decrease mortality in Lung cancer. Cancer is one of the major reasons for the increase in mortality rate globally. The biomedical image processing has better ability of detection in lung disease as it will be helpful in analyzing each image and monitoring the data. In the existing x-ray-based image of the lungs does irrelevant matching, it is restricted in starting stages of biomedical strategies which involve complex identification in Medical Image Processing. In this method, Computed Tomography (CT) is proposed for lung Image methodology to disease identification based on different stages. The preprocessing is done to get a fundamental axis by identifying pyramidal representation segmentation level and manipulating the images affected data which can be sent to feature extraction. Machine learning is to discover examples and highlights in images that measure the values dependent on application. The classification of Machine Learning based Reinforcement Learning Algorithms is a mathematical function and probability models from Medical Image Processing classify images into affected and non-affected parts which can be analyzed through the networking method using the Internet of Things (IOT), and it will store or monitor and display. The output result shows better efficiency in terms of sensitivity and accuracy using medical image processing technique.