alexa Content-Based Image Retrieval (CBIR) Based Computer-Aided Diagnosis (CAD) in Evaluation of Lung Nodules: A New Tool for Self-Learning and To Assist Radiologists in Diagnosing Lung Cancer| Abstract
ISSN-2155-9929

Journal of Molecular Biomarkers & Diagnosis
Open Access

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  • Opinion Article   
  • J Mol Biomark Diagn 2017, Vol 8(5): S2-033
  • DOI: 10.4172/2155-9929.S2-033

Content-Based Image Retrieval (CBIR) Based Computer-Aided Diagnosis (CAD) in Evaluation of Lung Nodules: A New Tool for Self-Learning and To Assist Radiologists in Diagnosing Lung Cancer

Mandeep Garg*, Nidhi Prabhakar, Sudipta Mukhopadhyay and Niranjan Khandelwal
Department of Radiodiagnosis, PGIMER, Chandigarh, , India
*Corresponding Author : Mandeep Garg, Department of Radiodiagnosis, PGIMER, Chandigarh, India, Tel: 0172-2756381, Email: [email protected]

Received Date: Jun 30, 2017 / Accepted Date: Jul 26, 2017 / Published Date: Jul 28, 2017

Abstract

Lung cancer is the leading cause of cancer related deaths in general population. Early diagnosis of malignant pulmonary nodule, can improve 5-year survival rate of lung cancer by upto 80%. There is increase in incidentally detected pulmonary nodules with the increased usage of diagnostic imaging modalities especially computed tomography (CT) of chest. Most often, physicians and trainee doctors have to depend on the experienced radiologists to confidently label these nodules as benign or malignant, thereby raising a need for some method, which could help them in self-learning and also could assist radiologists in ruling out malignancy with good certainty and confidence.

Keywords: Computed tomography (CT); Content-based image retrieval (CBIR)

Citation: Garg M, Prabhakar N, Mukhopadhyay S, Khandelwal N (2017) Content-Based Image Retrieval (CBIR) Based Computer-Aided Diagnosis (CAD) in Evaluation of Lung Nodules: A New Tool for Self-Learning and To Assist Radiologists in Diagnosing Lung Cancer. J Mol Biomark Diagn S2: 033. Doi: 10.4172/2155-9929.S2-033

Copyright: ©2017 Garg M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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