JOURNAL ARTICLE

Lung nodule detection in CT images using neuro fuzzy classifier

Abstract

Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.

Keywords:
Computer science Artificial intelligence Feature extraction Segmentation Lung cancer Pattern recognition (psychology) Computer-aided diagnosis Image segmentation Nodule (geology) Classifier (UML) Lung Computer vision Support vector machine Feature (linguistics) Radiology Pathology Medicine Biology

Metrics

46
Cited By
2.29
FWCI (Field Weighted Citation Impact)
15
Refs
0.87
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Lung Cancer Diagnosis and Treatment
Health Sciences →  Medicine →  Pulmonary and Respiratory Medicine
Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Brain Tumor Detection and Classification
Life Sciences →  Neuroscience →  Neurology

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