Application of ML/DL for improved Radiotherapy

Abstract

Now-a-days in the field of medical imaging, artificial intelligence (AI) has the ability to be a game-changer. Understanding the concepts and applications of radiomics, machine learning, and deep learning is necessary to knit design strategies that meet legal and privacy criteria, as well as to develop Artificial Intelligence algorithms that improve results, consistency, and performance. Deep neural networks outshine human or even other Machine Learning capacities in machine vision and speech recognition functions, owing to advances in computational resources with graphics processing unit (GPU) and the accessibility of huge data processing. These capabilities have recently been extended to healthcare issues such as computer-assisted analysis, sickness prediction, image segmentation, and image creation, among others. Radiologists can identify anomalies more quickly using computer-assisted identification. Medical photographs provide material that can be used to identify and diagnose a variety of diseases and disorders. So many methods allow radiologists to examine the inner structure, but these methods have sparked significant interest in a variety of studies. Radiomics is a new feature transforming approach for identifying medically important features in radiological images acquired that are impossible to identify with the naked eye. Machine learning and deep learning approaches are rapidly being studied to aid identification, with the aim of optimizing medical identification, care choices, and outcomes. An analysis workings in the rapidly growing domain of radiomics for cancer mortality identification is presented. From outside the domain of radiomics, research projects that identify methods that could be useful in the future to enhance feature extraction are also examined. Radiomics is a quickly developing area of medical image processing with enormous potential for assisting in diagnosis of cancer and treatment decision-making. Productive decision making is reliant on all in part on medical-device developers having expertise as well as computer scientists; this may be challenging due to existing or future advancements in computer vision and artificial intelligence; equally, it is essential to give designers the medical community the same tools they need to draw upon, train, and draw on their unique and effective algorithms there will be significant growth in the application of radiomics in the coming decades provided the expertise and learning systems that can be transferred to patient groups.