Classification Of Lung Cancer Using Lightweight Deep Neural Networks

Abstract

Early recognition of lung nodules is critical for the effective conclusion and treatment of lung cancer. Numerous scientists have attempted various strategies, for example, thresholding, pattern recognition technique, computer-aided diagnosis, backpropagation calculation, etc. As of late, convolutional neural networks track down promising applications in numerous areas. In a conventional computer-aided detection (CAD) system, categorization of lung nodule detection is a challenging task. Each step of the lung nodule detection process depends on the classifier, which leads to poor recognition rate and a high false-positive rate. A classification approach for lung nodules based on deep hybrid learning is presented to address these challenges. This study aims to assess the performance of several low-memory, lightweight deep neural net (DNN) designs for image processing. The proposed lightweight deep neural model outperforms with an accuracy of 85.21% with reasonable specificity and sensitivity trade-off. The proposed work is based on binary classification networks such as vanilla 2D CNN, 2D SqueezeNet, and 2D MobileNet to recognize lung cancer in patient CT images with and without early-stage lung cancer.