Lung Nodule Classification Techniques for Low Dose Computed Tomography (LDCT) Scan Images as Survey

Suganya, A; Mohanapriya, N.; Kalaavathi, B.

doi:10.5120/ijca2015907464

IJCA

2015

DOI: 10.5120/ijca2015907464

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Lung Nodule Classification Techniques for Low Dose Computed Tomography (LDCT) Scan Images as Survey

A Suganya¹,

N. Mohanapriya²,

B. Kalaavathi³

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2017

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Detection of Pulmonary Nodules in Thoracic CT images using Image Processing and Data Mining Techniques

Ramani¹,

Keerthana²,

Lakshmi³

2017

IJCA

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Pulmonary nodules are soft tissue masses present in the lung that can be seen in thoracic CT images. When the size of the nodules is large, immediate attention is required. Automatic detection of these large nodules would be appreciated by the medical professionals as it reduces labor and time. The lung nodules are usually extracted through image processing and classification techniques. In this work, lung nodules are detected through image clustering followed by classification of candidate nodules as either nodules or non-nodules. KMeans clustering is performed to delineate the candidate nodules. The features of the candidate nodules are extracted and fed as input to bagged random tree classifier for classification of nodules as true or not. The proposed system was tested on ELCAP dataset and an accuracy of 95.31% was achieved. The system has a social cause.

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Detection of Pulmonary Nodules in Thoracic CT images using Image Processing and Data Mining Techniques

Ramani¹,

Keerthana²,

Lakshmi³

2017

IJCA

View full text Add to dashboard Cite

show abstract

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Lung Nodule Classification Techniques for Low Dose Computed Tomography (LDCT) Scan Images as Survey

Cited by 1 publication

References 4 publications

Detection of Pulmonary Nodules in Thoracic CT images using Image Processing and Data Mining Techniques

Detection of Pulmonary Nodules in Thoracic CT images using Image Processing and Data Mining Techniques

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