The impact of transfer learning on lung cancer detection using various deep neural network architectures

Lung cancer is the primary reason for death among cancer patients. Early diagnosis of lung cancer in patients has a much higher chance of survival than when lung cancer has spread. In the last few years, computational intelligence-based techniques like deep learning have been designed to diagnose lung cancer at an early stage. This paper combines three types of optimizers with six deep learning models to conduct a performance comparison. This investigation focuses on six models AlexNet, GoogleNet, ResNet, Inception V3, EfficientNet b0, and SqueezeNet. The different models are assessed by comparing their performance with a stochastic gradient with momentum, Adam, and RMSProp optimization techniques. The study showed that CPU training takes time for training without GPU support. According to this study, the google net with Adam as optimizer gives Accuracy-92.08%, Precision-100%, Recall-86.89%, F1score-92.98%, FPR-0%, FNR-13.11%,outperforming the other deep learning architectures. When comparing the computational time for deep learning models, it is observed that Inception V3 takes the most time to train, and AlexNet takes the least time.