Application of Deep Learning in Radiation Therapy

Abstract

It is a computational approach, which uses a deep learning model with an architecture similar to that of biological brain networks, that has been trained using vast amounts of data. Deep reinforcement learning (DRL) is another name for deep learning (DL). By eliminating the need for direct programming in the process, DL functions as a middleman between data collection and meaningful knowledge. It has outperformed the vast majority of classification algorithms and is capable of learning data representations for a broad range of functions by itself. Deep learning applications in cancer include classification, feature extraction, object identification, picture interpretation and translation, sensitively and appropriately, and image annotation and labeling. The goal of this chapter is to gain a better understanding of the possible function of DL and how it may be used more successfully in radiation oncology. With the expansion of DL, a wide range of studies that led to the improvement of radiation oncology were explored more thoroughly than before. This article discussed medical imaging, image segmentation utilizing computers to assist diagnosis, computer-aided detection, treatment planning and delivery, quality assurance, treatment response, and treatment delivery. The studies that utilized DL were classified and organized according to the kind of radiation treatment used. The most current scientific achievements were chosen, and the therapeutic value of their results was assessed. Therapists may profit from employing a deep learning model since it will provide them with more precise and accurate solutions to their problems. A statistical approach to cancer patient safety is obvious, but implementing these concepts would require social adjustments at both the academic and industrial levels, which will take time. The goal of this chapter was to make it as accessible as possible to both radiotherapy and deep learning in order to encourage new collaboration between the two groups in the development of specialized radiation technologies.