@inproceedings{4e70ccc293e642e08df80e6c354372d1,
title = "Artificial neural network-based classification system for lung nodules on computed tomography scans",
abstract = "Lung cancer is the most common type of cancer among various cancers with the highest mortality rate. The fact that nodules that form on the lungs are in different shapes such as round or spiral in some cases makes their detection difficult. Early diagnosis facilitates identification of treatment phases and increases success rates in treatment. In this study, a holistic Computer Aided Diagnosis (CAD) system has been developed by using Computed-Tomography (CT) images to ensure early diagnosis of lung cancer and differentiation between benign and malignant tumors. The designed CAD system provides segmentation of nodules on the lobes with neural networks model of Self-Organizing Maps (SOM) and ensures classification between benign and malignant nodules with the help of ANN (Artificial Neural Network). Performance values of 90.63% accuracy, 92.30% sensitivity and 89.47% specificity were acquired in the CAD system which utilized a total of 128 CT images obtained from 47 patients.",
keywords = "ANN classification, CAD, CT images, Lung cancer, Lung nodule",
author = "Emre Dandil and Murat Cakiroglu and Ziya Eksi and Murat Ozkan and Kurt, {Ozlem Kar} and Arzu Canan",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014 ; Conference date: 11-08-2014 Through 14-08-2014",
year = "2014",
month = jan,
day = "12",
doi = "10.1109/SOCPAR.2014.7008037",
language = "English (US)",
series = "6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "382--386",
booktitle = "6th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2014",
}