A Review on Lung Cancer Diagnosis and Prediction Techniques

This review paper explores different methodologies for lung cancer diagnosis and prediction techniques, providing a comprehensive analysis of the literature on various strategies addressing the aforementioned issues. A systematic review approach was used to evaluate studies published between 2016 and 2023. Research publications from several reputable online libraries, including IEEE, ScienceDirect, and Springer Link, have undergone thorough and critical evaluation. There are two specific study problems that have been addressed. To address the questions, recent papers that were pertinent to the subjects were thoroughly studied. Throughout this research paper, we looked into numerous methods for enhancing the deep learning algorithm's capacity for prediction and also explored the area of multimodal data and its future scope. In the later section, research gaps and future challenges that have been found during the study are also discussed.