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+Computer-Vision-Lung-Cancer-Detection
+=====================================
+
+This code is part of the 2013 REU with Depaul University and University of Chicago.
+The image processing code was lead by Patrick Stein. 
+The machine learning code was lead by Ethan Smith.
+The results of this research were published at the 2013 International Conference on Machine Learning Applications.
+Paper can be found at http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6786163&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6786163
+
+This code makes an alteration in the process of diagnosing lung cancer nodules. The typical process is as follows:
+-The human expert (radiologist) reviews the CT scan and identifies the presence/absense of nodules.
+-The human draws a border around each nodule
+-The computer uses image processing to turn the contents of that border into data
+-The computer uses that data to make predictions about the nature of the nodule (malignant, etc.)
+-The human performs his own evaluation of the nodule
+-The human uses the computer's predictions as a second opinion, and re examines the CT scan.
+-The human makes his final decision and writes a report
+-The report is sent to an oncologist who uses it to make the final diagnosis
+
+The step where the radiologist draws the pixel by pixel border around the nodule is expensive due to the time required by a specialist doctor.
+The process used in this research speeds up this process, by replacing this 'hard' border with a simple dot identifying the center of the nodule, and then uses several imaging processing methods to form several 'weak segmentations'.
+Then the machine learning process learns which combination of these weak segmentations tends to be the best at identifying nodules.
+This weak segmentation is used, processed into data, and then used by machine learning classifiers to make predictions.
+
+In our experiments this method performed quite well and actually performed better than using the hard borders. 
+This can greatly aid research in automated cancer detection by reducing the cost needed to obtain data to train on.
+This can also reduce the final cost of healthcare.