Lung Cancer Detection and Classification using Machine Learning Algorithm
Main Article Content
Abstract
The Main Objective of this research paper is to find out the early stage of lung cancer and explore the accuracy levels of
various machine learning algorithms. After a systematic literature study, we found out that some classifiers have low accuracy and some are
higher accuracy but difficult to reached nearer of 100%. Low accuracy and high implementation cost due to improper dealing wi th DICOM
images. For medical image processing many different types of images are used but Computer Tomography (CT) scans are generally
preferred because of less noise. Deep learning is proven to be the best method for medical image processing, lung nodule detection and
classification, feature extraction and lung cancer stage prediction. In the first stage of this system used image processing techniques to
extract lung regions. The segmentation is done using K Means. The features are extracted from the segmented images and the classification
are done using various machine learning algorithm. The performances of the proposed approaches are evaluated based on their accuracy,
sensitivity, specificity and classification time.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.