A Novel Lightweight Lung Cancer Classifier through Hybridization of DNN and Comparative Feature Optimizer

Abstract

The likelihood of successful early cancer nodule detection rises from 68% to 82% when a second radiologist aids in diagnosing lung cancer. Lung cancer nodules can be accurately classified by automatic diagnosis methods based on Convolutional Neural Networks (CNNs). However, complex calculations and high processing costs have emerged as significant obstacles to the smooth transfer of technology into commercially available products. This research presents the design, implementation, and evaluation of a unique lightweight deep learning-based hybrid classifier that obtains 97.09% accuracy while using an optimal architecture of four hidden layers and fifteen neurons. This classifier is straightforward, uses a novel self-comparative feature optimizer, and requires minimal computing resources, all of which open the way for creating a marketable solution to aid radiologists in diagnosing lung cancer.