Classification of malignant and Benign Lung Using Probabilistic Neural Network

Authors(3) :-Asmitha Shree R, Sajitha M, Subha S

Lung Cancer is considered as one of the deadliest diseases among other lung disorders and cancer types and is the leading cause of cancer deaths worldwide. Lung cancer is a curable disease if detected in its early stages that makes up 13% of all cancer diagnoses and 27% of all cancer deaths. The objective of this paper is mainly focused on categorizing the patients Computed Tomography (CT) lung images as normal or abnormal. The images are subjected to segmentation to focus on detecting the cancerous region to classify. Effective feature selection and feature extraction is made by applying Watershed Transform and Principal Component Analysis. The emphasis is on the feature extraction stage to yield a better classification performance. The classification of CT images as benign or malignant is done using Machine Learning based Neural Network.

Authors and Affiliations

Asmitha Shree R
Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India
Sajitha M
Department of Computer Science and Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India
Subha S
Department of Computer Science and Engineering, Sri Krishna College of Technology Coimbatore, Tamil Nadu, India

Watershed transform, Gray Level Co-Occurrence Matrix, Probabilistic neural network.

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Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 307-311
Manuscript Number : CSEIT195287
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Asmitha Shree R, Sajitha M, Subha S, "Classification of malignant and Benign Lung Using Probabilistic Neural Network", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.307-311, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195287
Journal URL : http://ijsrcseit.com/CSEIT195287

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