Impact of Deep Learning and Applications in Biomedicine

Introduction

Deep learning is a new and quickly growing field of artificial intelligence. It aims to demonstrate reasoning based on a variety of information, using different Deep Neural Networks (DNNs) to detect information like images, speech, and text. Moreover, deep learning has two common features first, various layers of nonlinear computational units and second, superintending or learning of the elements introduced in each layer (Yu D [1]). In the 1980s the first deep learning systems were based on pseudo neuron or Artificial Neural Networks (ANN), and the true impact of deep learning was not evident and visible until 2006 (Fukushim, et al. [2-4]). From that time till now, deep learning has found applications and been used in diverse fields such as programmatic speech recognition, image recognition an engineering application of machine learning, the application of computational techniques to the analysis and synthesis of natural language and speech, drug making process, and bioinformatics (Cios K J, et al. [5-7]). In recent years, biomedical data has been greatly aided by the development of high-throughput technological innovations including genome sequencing, protein engineering, and clinical/medical images. Most importantly, powerful and efficient computers are needed to store, analyze and decode this large amount of biomedical information (Cios K J, et al. [5,8]).

The deep learning algorithmic systems collectively identified and enlightened these complex issues. Accordingly, the objective of this article is to bring forth an overview of deep learning methods for the local field of bioinformatics and biomedical informatics and to present some of the recent applications of deep learning in biomedicine. It is anticipated that this article will provide people an outline of deep learning and make it understandable that how it can be successfully used to examine biomedical information.

Current Metrics

Regardless of the enormous benefits of deep learning, there are still problems with its application in the biomedical field. Likewise, using background images, we show how deep learning can characterize the extent of diabetic retinopathy and attempt to identify wound or damaged area in multiple ways. In addition to high degree of accuracy and momentum, deep learning’s intelligent use of response fields gives it a big advantage in pattern recognition. Along with that, improved head-to-tail classification using deep learning also gives new ideas and throws light on characterizing and assorting pixels as injured or not. Whereas the application of deep learning to clinical/ medical images is still in the testing phase. When creating models, we want to get a lot of information, sometimes data with labels, directly in the order for classifying the pixels. Naming these medical images physically is difficult and hence necessitate seasoned professionals. Furthermore, these medical images are closely related to data security, so it is crucial to understand and protect the information. Also, biomedical information tends to be unbalanced when the amount of information in general categories is greater than the amount of information in other categories. Despite the balancing hurdles, the amount of information and data needed, and the nomenclature/ naming of biomedical information, deep learning entails and necessitates technological upgradation and innovation. In any case, discrete differences and changes in clinical images would indicate disease because they are different than the normal images. That is why, examining these images needs high resolution, fast processing and having the capacity of incorporating large memory. In addition, it is challenging to identify and discover a single evaluation metric for grouping and predicting biomedical information.

Alongside, different from other projects, it handles false trends to some extent and does not reject (many) false negatives in disease detection. In case of dealing with distinct and unalike, it is necessary to thoroughly test the model and adjust its informative quality as per the features and attributes of the data. Luckily, deeper coordinates can be accelerated in the inception modules (He K [9,10]), and higher degree of accuracy can be achieved in biomedical imaging studies (Yarlagadda D V K, et al. [11]). Also, collection of information, opinions, or work from a group of people, usually sourced via the Internet (crowdsourcing pathway) have started to lay the foundation for the collection of annotations, which could be a significant tool in the near future. Hence, these two engines will facilitate the use of deep learning in the field of biomedical informatics (Albarqouni S, et al. [12-14]).

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