Detection of Lung Nodules in Computed Tomography Image using Deep Machine Learning: A Review

12 Pages Posted: 17 Jul 2019 Last revised: 30 Sep 2019

See all articles by Mahender G. Nakrani

Mahender G. Nakrani

CSMSS Chh. Shahu College of Engineering - Department of Electronics & Telecommunication Engineering

Ganesh Sable

G S Mandal’s Maharashtra Institite of Technology, Aurangabad, 431002, India

U. B. Shinde

CSMSS Chh. Shahu College of Engineering - Department of Electronics & Telecommunication Engineering

Date Written: May 18, 2019

Abstract

Lung Nodules detection is very critical in detection of early stage lung cancer. A radiologist tries to diagnoses the clinical chest computed tomography (CT) scans by detecting lung nodules in them. This task is rigorous and becomes even more difficult due to the complex structure and anatomy of lung parenchyma region. To assist radiologists in correct diagnosis of CT scan images, many Computer-aided detection (CAD) algorithms were developed and proposed. After the success of deep convolutional neural network (D-CNN) for classification of images, D-CNN has found its way into lung nodules detection systems. D-CNN has demonstrated better results and performances than traditional machine learning based lung nodules detection algorithms. In this paper, we will discuss about different D-CNN proposed for lung nodules detection and compare the results and performances of these detection algorithms. We will also discuss about the D-CNN which can be used to further improve the results of lung nodules detection.

Keywords: Lung Nodules, Convolutional Neural Network, Nodules detection, Classification, False positive reduction

JEL Classification: Y60

Suggested Citation

G. Nakrani, Mahender and Sable, Ganesh and B. Shinde, U., Detection of Lung Nodules in Computed Tomography Image using Deep Machine Learning: A Review (May 18, 2019). Proceedings of International Conference on Communication and Information Processing (ICCIP) 2019, Available at SSRN: https://ssrn.com/abstract=3421475 or http://dx.doi.org/10.2139/ssrn.3421475

Mahender G. Nakrani (Contact Author)

CSMSS Chh. Shahu College of Engineering - Department of Electronics & Telecommunication Engineering ( email )

Aurangabad
India

Ganesh Sable

G S Mandal’s Maharashtra Institite of Technology, Aurangabad, 431002, India ( email )

U. B. Shinde

CSMSS Chh. Shahu College of Engineering - Department of Electronics & Telecommunication Engineering ( email )

Aurangabad
India

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