An Improved Computer Aided System for Lung Cancer Detection using Image Processing Techniques

DOI

10.2991/978-94-6463-196-8_2How to use a DOI?

Keywords

ROI; K-means clustering; CAD-computer aided system; Lung; Nodules

Abstract

Early detection and prevention is the only way to treat lung cancer to avoid the loss of life. Where Computed Tomography (CT) screening is viewed as perhaps the best technique for discovering the early indications of lung malignant growth. The primary goal of this study is nodule detection and classification of collected CT scans images as benign or malignant. Sometimes some human errors can occur in the checking of a long series of CT slices of a single patient manually. This automated system (CAD-Computer Aided System) can help to radiologist or doctors to know the current stages and condition of the disease to diagnose correctly and quickly on a single click which will useful for radiologists and doctors to avoid the serious disease stage. The key four processes of our proposed system are input CT images, pre-processing, features extraction, and classification. In the proposed approach firstly we read all the CT image database (70 thoracic lung CT scans) having Dicom format then applied some pre-processing techniques of Matlab to enhance the image quality and obtained texture features. Using texture features, we extracted several features. At the end, we classified the dataset as benign or malignant using the K-means clustering method, and we achieved an accuracy of 92.8 percent.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Manoj Mhaske
AU  - Ramesh Manza
AU  - Pallavi Pradhan
AU  - Kavita Khobragade
PY  - 2023
DA  - 2023/08/10
TI  - An Improved Computer Aided System for Lung Cancer Detection using Image Processing Techniques
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 4
EP  - 13
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_2
DO  - 10.2991/978-94-6463-196-8_2
ID  - Mhaske2023
ER  -