Indian Journal of Science and Technology

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An Efficient and Complete Automatic System for Detecting Lung Module


Affiliations
1 Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India
3 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India
 

Objectives: To make a fully automated algorithm that is based on simple and quick steps, which produces consistent output for the same inputs. Methods/Statistical Analysis: For thorax and lung segmentation, region growing based method is used to segment the region of interest. The missing parts of the lungs are reconstructed using morphological operations. After that, nodules are detected based on the features of the reconstructed image. Artificial Neural Network has been used for classifying the images. Findings: An aggregate of 100 pictures with determination of 512 × 512 pixels with eight bits for every shading channel are caught. 90% affectability was obtained with 0.05 false positives for each picture. Application/Improvements: This framework distinguishes the phase of lung malignancy. The outcomes demonstrate that the tumors are of various measurements. By estimating the measurements of the tumor the lung disease stage can be recognized precisely utilizing the proposed technique. The outcomes indicate great potential for lung growth identification at beginning time.
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  • An Efficient and Complete Automatic System for Detecting Lung Module

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Authors

S. Balakrishnan
Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India
J. Janet
Department of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India
K. Sujatha
Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India
S. Sheeba Rani
Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore – 641008, Tamil Nadu, India

Abstract


Objectives: To make a fully automated algorithm that is based on simple and quick steps, which produces consistent output for the same inputs. Methods/Statistical Analysis: For thorax and lung segmentation, region growing based method is used to segment the region of interest. The missing parts of the lungs are reconstructed using morphological operations. After that, nodules are detected based on the features of the reconstructed image. Artificial Neural Network has been used for classifying the images. Findings: An aggregate of 100 pictures with determination of 512 × 512 pixels with eight bits for every shading channel are caught. 90% affectability was obtained with 0.05 false positives for each picture. Application/Improvements: This framework distinguishes the phase of lung malignancy. The outcomes demonstrate that the tumors are of various measurements. By estimating the measurements of the tumor the lung disease stage can be recognized precisely utilizing the proposed technique. The outcomes indicate great potential for lung growth identification at beginning time.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i26%2F130559