Contribution of Deep Learning in Bioinformatics

Abstract

Deep learning, a machine learning methodology established on artificial neural networks (ANN), in recent years has emerged as an influential tool for machine learning, with the potential to transform the future of AI. Deep learning technologies have advanced to the point that new paradigms for obtaining end-to-end learning models from complicated data have emerged. While artificial intelligence (AI) is the greatest recognized of the technological terminology, AI is the subset of deep learning in healthcare that has disruptive potential and adds a new layer to medical technology solutions.According to the research, we believe that deep learning technologies might be the key to transforming large amounts of biological data into better human health. The use of deep learning (DL) in medical diagnostics has the potential to revolutionize the field. The diagnostic accuracy of DL, on the other hand, is debatable. Our goal was to see how accurate DL algorithms were in detecting pathology in medical images. Medical imaging technologies, medical data analysis, medical diagnostics, and healthcare all stand to benefit greatly from these advancements. Based on COVID-19, we examine deep learning models, methodologies, and outcomes.