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Investigation of Deep Features in Lung Nodule Classification

Cancer is one of the deadliest diseases that affect people worldwide, regardless of their socioeconomic situation. Head and neck cancer, brain cancer, stomach cancer, breast cancer, and pancreatic cancer are some of the different types of cancer. Lung cancer has the highest occurrence and fatality rate among all cancer diseases worldwide. It has become more prevalent in developing countries due to increased air pollution. As a result of this terrible sight, lung cancer screening and early diagnosis are now more important than ever. Taking Computed Tomography (CT) images of the entire chest region and analyzing them for any abnormalities is the most common method for detecting lung cancer at its initial stage. In the clinical diagnosis of many diseases, transfer learning and deep learning are becoming increasingly important. Lung nodules, categorized as malignant or benign, are radiographic indications of lung cancer. The application of transfer learning and deep learning in the Computed Aided Diagnosis (CAD) system to aid doctors in classifying lung nodules is proposed in this paper. Convolutional Neural Networks (CNNs) such as VGG16, VGG19, and ResNet50 are used as feature extractors in this study. In the classification of lung nodules, the performance of these deep features is explored.