Study
NLST
(Learn more about this study)
Project ID
NLST-277
Initial CDAS Request Approval
Jan 30, 2017
Title
Lung Cancer Detection using Data Analytics and Machine Learning
Summary
Our study aims to highlight the significance of data analytics and machine learning (both burgeoning domains) in prognosis in health sciences, particularly in detecting life threatening and terminal diseases like cancer. Here, we consider lung cancer for our study. For this purpose, preexisting lung cancer patients’ data are collected to get the desired results. Data set (in the form of diagnostic images) is run past Matlab for analysis and forecasting. Image processing is employed for this purpose. Medical image segmentation and classification are done to achieve this. The patients’ Computed Tomography(CT) lung images are categorised as normal or abnormal. The abnormal images are subjected to segmentation to focus on the tumor portion. Classification depends on features extracted from the images. The emphasis is on the feature extraction stage to yield better classification performance. This information is then fed to machine learning algorithms to discern a pattern that can give some good insights into what combination of features are most likely to result in an abnormality. The ultimate goal is to identify effective and common methods for classification using some well established machine learning algorithms like FPCM and its improved versions.
Aims
We hope to use real time chest computer tomography images of lung cancer patients to achieve a system which can detect lung cancer with a reasonably high degree of efficiency. The empirical results are expected to demonstrate the advantage of the proposed CAD system for detecting lung cancer.
Collaborators
Ms. Kajal Jewani (Assistant Professor, Department of Computer Engineering, Vivekanand Education Society's Institute of Technology)
Chetan Rawool (student, Department of Computer Engineering, Vivekanand Education Society's Institute of Technology)
Dinesh Tolani (Department of Computer Engineering, Vivekanand Education Society's Institute of Technology)
Deepesh Bathija (Department of Computer Engineering, Vivekanand Education Society's Institute of Technology)
