Publication Type : Journal Article
Publisher : International Journal of Recent Technology and Engineering (IJRTE)
Source : International Journal of Recent Technology and Engineering (IJRTE), Blue Eyes Intelligence Engineering & Sciences Publication, Volume 8, Issue 2S3 (2019)
Url : https://www.ijrte.org/wp-content/uploads/papers/v8i2S3/B11940782S319.pdf
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : Lung cancer is one of the most common deadliest
disease which has highest death rate as per the recent medical
research. However, research indicates that early detection of lung
cancer improves chances of survival. The disease is identified
using nodules attached to lung walls and lung parenchyma.
Nodules plays significant role in identifying cancer in lungs. The
proposed approach to determine lung nodules has three stages
preprocessing, feature extraction and classification.
Segmentation is the preprocessing technique involves two phases
namely lung parenchyma segmentation and lung nodules
segmentation. Then, texture features and geometric features are
extracted using feature extraction algorithms. Lastly, using
classification techniques the nodules are classified as benign or
malign. TCIA dataset was used for validation of the proposed
approach. Form the dataset, CT images were used which have
high density resolution and adequate information which helps to
find every small detail easily. The proposed method helps in
improving accuracy to find number of the lung nodules in lung
region and also helps is differentiating benign and malign
nodules using CNN architecture. Different classifiers such as
SVM, MLP and CNN classifiers are used in comparison analysis.
As the result, we conclude that the approach of feature extraction
with CNN decreases the false positive rate significantly compared
to the existing classification approaches.
Cite this Research Publication : M. .k, .K, A., and Dr. N. Neelima, “Research on different classifiers for early detection of lung nodules”, International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2S3, 2019.