@inproceedings{f588a6506fae411fa597c033a0655115,
title = "Detection of lung nodules using unsupervised machine learning method",
abstract = "Machine learning methods are now becoming a popular choice in many computer-aided bio-medical image analysis systems. It reduces the efforts of a medical expert and helps in making correct decisions. One of the main applications of such systems is the early detection of lung cancerous nodules using Computed Tomography (CT) scan images. Here, we have used a new method for automated detection of lung cancerous/non-cancerous nodules. It is a modularity maximization based graph clustering method. The clustering is done based on the different region{\textquoteright}s grayscale values of the CT scan images. The clustering algorithm is capable of detecting nodules of size as small as 4 pixels in two dimension (2D) or 9 voxels in three dimensional (3D) data. The advantage of nodule detection is that it can be used as an extra feature for many supervised learning algorithms especially for those Convolutional Neural Networks (CNN) based architectures where pixel-wise segmentation of data might be required.",
keywords = "CNN, CT scan, Graph clustering, Lung cancer, Machine learning, Modularity, Segmentation",
author = "Raj Kishore and Manoranjan Satpathy and Parida, \{D. K.\} and Zohar Nussinov and Sahu, \{Kisor K.\}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Computational Vision and Bio Inspired Computing, ICCVBIC 2019 ; Conference date: 25-09-2019 Through 26-09-2019",
year = "2020",
doi = "10.1007/978-3-030-37218-7\_52",
language = "English",
isbn = "9783030372170",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "463--471",
editor = "S. Smys and Tavares, \{Jo{\~a}o Manuel R.S.\} and Balas, \{Valentina Emilia\} and Iliyasu, \{Abdullah M.\}",
booktitle = "Computational Vision and Bio-Inspired Computing, ICCVBIC 2019",
}