JOURNAL ARTICLE

Classification of lung image and nodule detection using fuzzy inference system

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:
Artificial intelligence Computer science Pattern recognition (psychology) Computer vision Feature (linguistics) Support vector machine Feature vector Feature extraction Image (mathematics) Contextual image classification Fuzzy logic Inference

Metrics

50
Cited By
1.89
FWCI (Field Weighted Citation Impact)
6
Refs
0.84
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Lung Cancer Diagnosis and Treatment
Health Sciences →  Medicine →  Pulmonary and Respiratory Medicine
Medical Image Segmentation Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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