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Automatic Lung Nodule Detection in CT Images Using Convolutional Neural Networks
Furqan SHAUKAT Kamran JAVED Gulistan RAJA Junaid MIR Muhammad Laiq Ur Rahman SHAHID
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2019/10/01
Online ISSN: 1745-1337
Type of Manuscript: PAPER
CAD, CT, CNN, feature extraction, supervised learning,
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One of the major causes of mortalities around the globe is lung cancer with the least chance of survival even after the diagnosis. Computer-aided detection can play an important role, especially in initial screening and thus prevent the deaths caused by lung cancer. In this paper, a novel technique for lung nodule detection, which is the primary cause of lung cancer, is proposed using convolutional neural networks. Initially, the lung volume is segmented from a CT image using optimal thresholding which is followed by image enhancement using multi-scale dot enhancement filtering. Next, lung nodule candidates are detected from an enhanced image and certain features are extracted. The extracted features belong to intensity, shape and texture class. Finally, the classification of lung nodule candidates into nodules and non-nodules is done using a convolutional neural network. The Lung Image Database Consortium (LIDC) dataset has been used to evaluate the proposed system which achieved an accuracy of 94.80% with 6.2 false positives per scan only.