DFCV: a framework for evaluation deep learning in early detection and classification of lung cancer

The deep learning (DL) classification technique is extensively researched and considered for early lung cancer diagnosis. Despite the encouraging performance reported in the literature, DL models face several challenges to be deployed in real-life systems. These include the DL-Models' stability, the nodule structure's complexity, the lack of proper lung segmentation technique, high false-positive results, and the availability of publically shared medical imaging data. This paper investigates, identifies, and intensively studies DL approaches that yield high performance in the classification of Lung Cancer. We reviewed 338 articles, of which 37 met the inclusion criteria we have set for the proposed framework. In addition, we propose and evaluate a framework to govern the DL model selection and deployment process in real-world systems. The framework consists of four main components; Data, Feature Selection, Classification Technique, and View (DFCV). We discuss the efficiency and the importance of the proposed DFCV framework on 37 state-of-the-art research papers in the field of deep learning-based lung cancer classification systems. The DFCV framework could represent a guide for DL-based systems selection and deployment in medical centers for lung cancer.