Residual attention network based hybrid convolution network model for lung cancer detection

Abstract

Lung cancer is one of the dangerous diseases that cause shortness of breath and death. Automatic lung cancer disease identification is a challenging operation for researchers. This paper, presents an effective lung cancer diagnosis system using deep learning with CT images. It also decreases lung cancer’s misclassification. Initially, the input images are gathered from online resources. The collected CT images are given to the detection stage. Here, we perform the detection using a Multi Serial Hybrid convolution based Residual Attention Network (MSHCRAN). Using a deep learning framework lung cancer detection using CT images is effectively detected. The performance of the developed lung cancer detection system is compared to other conventional lung cancer detection models According to the analysis, the implemented deep learning-based detection of lung cancer system had a precision higher than 95.75% compared to CNN with 90.04%, ResNet with 89.62%, LSTM with 92%, and CRAN with 93.4% using dataset-1. The analysis with Dataset-2 shows a precision of 90.43% with CNN, ResNet with 90.12%, LSTM with 92%, and CRAN with 93.7%, with the proposed method precision of 95.8%.