Emerging computational intelligence based techniques for lung cancer diagnosis and classification on chest CT scan images: a comprehensive survey

Worldwide lung cancer is a significant reason for death resulting from cancer with early diagnosis crucial for enhancing patient results. This comprehensive survey looks at the most recent developments in methods for detecting lung cancer by using chest CT scan images. The study describes a broad variety of approaches includes methods for machine learning such random forests support vector machines logistic regression and k-nearest neighbors in addition to deep learning frameworks such as variational autoencoders recurrent neural networks convolutional neural networks and generative adversarial networks. Additionally the survey explores hybrid models that combine deep learning and machine learning with nature-inspired optimization techniques to enhance performance. All the techniques discussed in this paper mainly focus on the diagnosis of NSCLC i.e. non-small cell lung cancer as it is more prevalent. The paper also reviews multiple advanced techniques used in diagnosis of lung cancer, including 3D-CNN i.e. Convolutional Neural Networks, multimodal logistic regression models and Cyclic Variational Autoencoders. It highlights key publicly available datasets frequently used in this research area such as LIDC-IDRI (Lung Image Database Consortium and Image Database Resource Initiative), LUNA16 (Lung Nodule Analysis 2016), the Kaggle lung cancer dataset, NSCLC Radiogenomics and the NIH (National Institutes of Health) chest X-ray database. This survey provides a detailed comparison of each technique, describing their advantages, limitations, and reported performance metrics, especially in terms of classification accuracy. Transfer learning with Vision Transformer achieves the highest accuracy of 94.6%, while 3D Convolutional Neural Network (3D -CNN) achieves an accuracy of 93.7%, both of which are showcasing highest performance on applicable datasets. Furthermore, the research demonstrates the potential of emerging techniques like federated learning and explainable AI in addressing challenges pertaining to data privacy and model interpretability. This survey paper reviews several techniques and finds that deep learning is the most extensively researched area in lung cancer diagnosis. This approach is not only widely used but also exhibits notable success in identifying and categorizing lung cancer with a high degree of accuracy.