Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/53590
Title: Pulmonary Tumor Detection by virtue of GLCM
Authors: Kanagaraj, G
Kumar, P Suresh
Keywords: Gray Level Co-Occurrence Matrix;Microscopic lung biopsy;Artificial neural network algorithm
Issue Date: Feb-2020
Publisher: NISCAIR-CSIR, India
Abstract: As per the technical evolution and latest trend, Image processing techniques has become a boon in medical domain especially for tumor detection. Presence of tumor in Lungs which leads to lung cancer is a prominent and trivial disease at 18%. This is important to be detected at early stage thereby decreasing the mortality rate. The survival rate among people increased by early diagnosis of lung tumor. Detection of tumor cell will improve the survival rate from 14 to 49%. The aim of this research work is to design a lung tumor detection system based on analysis of microscopic image of biopsy using digital image processing. This can be done using Gray Level Co-Occurrence Matrix (GLCM) method and classified using back propagation neural network. This method is used for extracting texture features based on parameters such as contrast, correlation, energy, and homogeneity from the lung nodule. The microscopic lung biopsy images are classified into either cancer or non-cancer class using the artificial neural network algorithm. The proposed system has proven results in lung tumor detection and diagnosis.
Page(s): 132–134
URI: http://nopr.niscair.res.in/handle/123456789/53590
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.79(02) [February 2020]

Files in This Item:
File Description SizeFormat 
JSIR 79(2) 132-134.pdf297.17 kBAdobe PDFView/Open


Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.