Classification of lung image and nodule detection using fuzzy inference system

Author

Roy, Tanushree Sinha ; Sirohi, Neeraj ; Patle, Arti

Author_Institution

Comput. Sci. & Eng. Dept., IMS Eng. Coll., Ghaziabad, India

fYear

2015

fDate

15-16 May 2015

Firstpage

1204

Lastpage

1207

Abstract

The objective of this paper is to classify likely cancerous and noncancerous lung image and to detect the location of the nodule in the lung image provided by CT scan. The novelness of this paper is to provide better accuracy and assists radiologist to analyze CT scan images of lung accurately. This efficient proposed method consists of image enhancement, extracting region of interest using Active Contour Model, extracting spatial features from segmented image, train those feature vectors and classify the test image through Fuzzy Inference System. This proposed method performance is compared with one of the most efficient and popular existing method Support Vector Machine and shows better accuracy of 94.12%.

Keywords

computerised tomography; feature extraction; fuzzy reasoning; image classification; image enhancement; image segmentation; medical image processing; object detection; support vector machines; CT scan image analysis; active contour model; cancerous lung image classification; feature vector training; fuzzy inference system; image enhancement; image segmentation; nodule detection; noncancerous lung image classification; region of interest extraction; spatial feature extraction; support vector machine; Active contours; Cancer; Computed tomography; Feature extraction; Image segmentation; Lungs; Support vector machines; Active Contour Model; Fuzzy Inference System; Support Vector Machine; image enhancement or preprocessing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication & Automation (ICCCA), 2015 International Conference on

Conference_Location

Noida

Print_ISBN

978-1-4799-8889-1

Type

conf

DOI

10.1109/CCAA.2015.7148560

Filename

7148560