Lungs Cancer Detection Using 3DConvolutional Neural Network

Abstract

Lung cancer is one of the type malignant cancer. It occurs in a human body as a result of the uncontrolled growth of cells in lung tissues. Upon diagnoses the death rate of lung cancer patients is high as compared to survival rate. The symptoms of lung cancer manifest only in the advanced stages, therefore it’s extremely difficult to detect in its initial stage. Computed tomography (CT) scans have made lung cancer detection possible in its initial stage which is helpful for both diagnosis as well as its treatment. Computer aided detection (CAD) systems are functional for malignant nodule detection. A CAD system for lung cancer classification uses pre-processed 3D CT scan image slices given to 3D-CNN with rectified linear unit (ReLU) to classify malignant nodules. ReLU function does not trigger all the neurons at the same time which makes it proficient and easy for approximation. Although ReLU falls on the negative side of the graph, the weights are not updated during backward propagation because the slope is zero. Due to this fact, dead neurons are generated, which are never activated. In addition, currently no particular 3D CNN algorithm is available that can provide a generic classification of lung cancer nodule detection. Existing CAD models and state-of-the-art methods are available but they have lack of configurability. In this work, we have performed comprehensive analysis and have discussed each step in detail in order to develop a configurable, efficient method that can work with generic dataset with minimum pre-processing. Our proposed method allows users to select between pre-processing, feature extraction and activation function algorithms and help find the configuration which results in minimum error for a particular dataset to achieve high accuracy and optimistic solution.