A Comparative Study on Pre-Training Models of Deep Learning to Detect Lung Cancer - Aluka, Madhavi; Ganesan, Sumathi; Reddy P., Vijay Pal (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Detection of lung cancer using neural network-based systems has seen a reasonable improvement. However, the possibility of false cancer detection seems to be a worrying factor in recent times due to various technical reasons. Recent research programs revealed that machine learning (ML) based techniques were also found to make a greater contribution to lung cancer detection. However, deep learning (DL) techniques seem to provide enhanced accuracy for various medical research areas. Therefore, in this work different types of DL pre-trained prediction models are tested to study the accuracy of each model. The pre-trained models are applied to the dataset consisting of nearly 5000 images consisting of cancerous and non-cancerous data. Particularly, VGG-16, Inception V3, and ResNet50 are the Transfer Learning models used in this study for comparative analysis. The results show a reasonable accuracy using the VGG-16 model with fine-tuning and the image augmentation obtained greater accuracy of 96% and 93% for training data and validation data respectively.