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Pulmonary Tumor Detection by virtue of GLCM


Affiliations
1 Department of Electronics & Communication Engineering, AVS Engineering College, Salem-003, India
2 Department of Electrical& Electronics Engineering, Mahendra Engineering College (A), Namakkal, India
 

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.

Keywords

Gray Level Co-Occurrence Matrix, Microscopic Lung Biopsy, Artificial Neural Network Algorithm
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  • Pulmonary Tumor Detection by virtue of GLCM

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Authors

G. Kanagaraj
Department of Electronics & Communication Engineering, AVS Engineering College, Salem-003, India
P. Suresh Kumar
Department of Electrical& Electronics Engineering, Mahendra Engineering College (A), Namakkal, 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.

Keywords


Gray Level Co-Occurrence Matrix, Microscopic Lung Biopsy, Artificial Neural Network Algorithm

References