PCA-F-SHCNNet: Principal Component Analysis-Fused-Shepard Convolutional Neural Networks for lung cancer detection and severity level classification

Abstract

Lung cancer is one of the leading causes of cancer-related deaths worldwide. Therefore, lung cancer early detection is important to reduce the serious stage by implementing better treatment plans. While chest X-rays are commonly used for lung cancer detection, they are often not sensitive enough to detect early-stage cancers, in previous researches. Hence, to improve detection as well as classify lung cancer severity level, an innovative scheme is developed in this research using the Principal Component Analysis-Fused-Shepard Convolutional Neural Networks (PCA-F-ShCNNet) model, which is obtained by the amalgamation of the Principal Component Analysis Network (PCANet) and Shepard Convolutional Neural Networks (ShCNN). First, the input Computed Tomography (CT) image is pre-processed by utilizing Adaptive Weiner Filtering (AWF) and then, the segmentation is performed using U-Net. Afterwards, lung nodule is identified by employing a grid-based scheme and then a process of feature extraction is performed. Finally, the detection of lung cancer is performed by PCA-F-ShCNNet, where the layers will be modified as well as classification of severity level is executed by employing the same PCA-F-ShCNNet. Additionally, the developed PCA-F-ShCNNet method achieved superior accuracy, F-measure, and precision of 91.566 %, 90.490 % and 92.598 %, when compared to other existing approaches, such as Convolutional Neural Network-based Ebola optimization search algorithm (CNN-EOSA), Wavelet Partial Hadamard Transform-based optimal Support Vector Machine (WPHT-OSVM), Cuckoo Search Optimization, CNN, Local Binary Pattern (CSO + CNN + LBP), multi-round transfer learning and modified Generative Adversarial Network (MTL‐MGAN), Improved Deep Neural Network (IDNN), and Grey Wolf Optimization Algorithm and Recurrent Neural Network (GWO + RNN).