Classification of non-small cell lung cancer using one-dimensional convolutional neural network

Non-Small Cell Lung Cancer (NSCLC) is a major lung cancer type. Proper diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces problems because of the limited availability of tissue samples. Machine learning methods may play a vital role in such cases. 2D or 3D Deep Neural Networks (DNNs) has been the predominant technology in this domain. Contemporary studies tried to classify NSCLC tumors as benign or malignant. The application of 1D CNN in automated staging and grading of NSCLC is not very frequent. The aim of the present study is to develop a 1 D CNN model for automated staging and grading of NSCLC. The updated NSCLC Radiogenomics Collection from The Cancer Imaging Archive (TCIA) was used in the study. The segmented tumor images were fed into a hybrid feature detection and extraction model (MSER-SURF). The extracted features were clubbed with the clinical TNM stage and histopathological grade information and fed into the 1 D CNN model. The performance of the proposed CNN model was satisfactory. The accuracy and ROC-AUC score were higher than the other leading machine learning methods. The study also did well compared to state-of-the-art studies. The proposed model shows that 1D CNN is equally useful in NSCLC prediction like a conventional 2D/3D CNN model. The model may further be refined by carrying out experiments with varied hyper-parameters. Further studies may be conducted by considering semi-supervised or unsupervised learning techniques. (C) 2020 Elsevier Ltd. All rights reserved.