Detecting Lung Cancer Lesions in CT Images using 3D Convolutional Neural Networks

Early diagnosis of lung cancer is very important in improving patients life expectancies. Due to the high number of Computed Tomography (CT) images, fast and accurate diagnosis is difficult for radiologists. Therefore, there is an increasing demand for Computer-Aid Diagnosis (CAD) lung cancer. The core of all lung cancer detection systems is the distinction between cancer and non-cancerous tissues. This operation is performed in the false positive reduction phase, which is one of the most critical part of the lung cancer detection systems. The primary objective of this paper is to present a new method based on 3D Convolutional Neural Networks (CNN) that can reduce the false positives rate while providing a high sensitivity in detecting lung cancer lesions. We obtained 91.23% accuracy for 3.99 false positive per scan using a new method for fusion. The reason for accuracy improvement while reducing the false positive rate is by taking advantage of knowledge obtained from the classifiers in using a new fusion method.