Artificial Intelligence Meets Cancer Imaging The Promise and Progress of Deep Learning in Diagnosis

Abstract ObjectiveThe rapid growth of high-resolution medical imaging has been a boon to cancer diagnosis, providing essential information to help radiologists make more objective decisions. This paper reviews the recent advances and prospects of deep learning-based computer-aided diagnosis (CAD) systems in various cancer imaging modalities, especially in tumour segmentation and classification, as well as their clinical application in oncology. MethodsIn this study, we aimed to review the performance of deep learning techniques compared to non-deep learning-based methods in CAD for various types of imaging studies. Investigated areas involved in tumour segmentation, classification accuracy, and integration of the CAD system into clinical workflows. It also looked at these architectures (specifically convolutional neural networks CNNs) as they applied to cancer imaging. ResultsTraditional machine learning approaches achieved their best accuracy in tumour segmentation and classification tasks, but deep learning methods outperform them significantly. These systems have proven adept at analyzing more complex, often multiparametric, imaging data associated with greater diagnostic accuracy. Furthermore, the rapid progress made in deep learning models proposed in the literature creates opportunities for real-time and automated CAD systems with great promise for clinical adoption. ConclusionThis work on deep learning for medical imaging leads to the development of computer-aided diagnosis (CAD) systems that revolutionize cancer imaging, increasing diagnostic throughput and accuracy. Deep learning is a transformative technology that has enabled better cancer detection and treatment planning than classical methods. Future research and validation are important for establishing the clinical use of these systems in practice.