Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks - Togacar, Mesut; Ergen, Burhan; Comert, Zafer (Elsevier, 2020)
Lung cancer is a disease caused by the involuntary increase of cells in the lung tissue. Early detection of cancerous cells is of vital importance in the lungs providing oxygen to the human body and excretion of carbon dioxide in the body as a result of vital activities. In this study, the detection of lung cancers is realized using LeNet, AlexNet and VGG-16 deep learning models. The experiments were carried out on an open dataset composed of Computed Tomography (CT) images. In the experiment, convolutional neural networks (CNNs) were used for feature extraction and classification purposes. In order to increase the success rate of the classification, the image augmentation techniques, such as cutting, zooming, horizontal turning and filling, were applied to the dataset during the training of the models. Because of the outstanding success of AlexNet model, the features obtained from the last fully-connected layer of the model were separately applied as the input to linear regression (LR), linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), k-nearest neighbor (kNN) and softmax classifiers. A combination of AlexNet model and k NN classifier achieved the most efficient classification accuracy as 98.74 %. Then, the minimum redundancy maximum relevance (mRMR) feature selection method was applied to the deep feature set to choose the most efficient features. Consequently, the success rate was yielded as 99.51 % by reclassifying the dataset with the selected features and k NN model. The proposed model is consistent diagnosis model for lung cancer detection using chest CT images. (c) 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
Keywords: Biomedical image processing; Diagnosis system; Lung cancer; Convolutional neural network; Machine learning