Molecular imaging analysis in cancer using deep learning: a review

Purpose Molecular imaging (MI) empowers the representation and quantitative investigation of the methodologies at molecular as well as cellular levels. It is important for the early discovery of malignant growth. This, in turn, helps us with the diagnosis of patients followed by treatment planning. As of late, deep learning (DL) is generally utilized in therapeutic imaging investigation, because it defeats the limits of visual estimation and conventional artificial intelligence (AI) systems by removing less useful features. There is still work to be done in terms of research and methodology. Methods Research on MI in malignant growth analysis utilizing DL strategies is additionally expanding effectively. A review of these recent technologies is inevitable at this juncture. In this review paper, the uses of DL in MI for the tumor segmentation, classification, and survival forecast are highlighted. The primary focus of this review is on state-of-the-art approaches in cancer classification based on the DL. In this context, the recent trends and challenges in the field are also discussed in the accompanying angles. Some baseline methods are compared. Results Results are analyzed in terms of various standard metrics. Additionally, we present some future scopes wherein experts may develop dominant DL models for improved execution in the area of the molecular image analysis. Conclusion This article may be useful for researchers to explore many more ideas related to DL-based classification to detect and analyze malignant growth (cancer) in MI.