Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise; second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer.