Lung CT Image Classification using Deep Neural Networks for Lung Cancer Detection

Abstract

In Recent years, image processing strategies are broadly utilized in a few restorative territories for image improvement in prior division and treatment stages, where the time factor is imperative to find the variation from the norm issues in target pictures, particularly in different malignant growth tumors, for example, lung disease. Lung cancer is the most important disease cause high mortality rate. And computer-aided diagnosis can be useful for physicians to accurately identify the cancer cells. Many computer-aided methods have been studied and applied using image processing and machine learning. But, they are not acceptable for a health-based classification models to have high false positive and true negative rates as it they can devastate lives through false diagnosis. To reduce the effect of them in classification, to perform experiments JSRT data set is considered as it is the most widely used benchmark data set. The proper segmentation of lung tumor from X-ray, CT-scan or MRI these are the stepping stone towards automated diagnosis system for lung cancer detection. Our detection is to train this neural network using volumes with tumor size and position. In recent techniques like machine learning can predict lung cancer but this technique is not suitable for predicting segmentation of images in that particular area.