Abstract
The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.
Highlights
Machine learning has been notably identified as less used in medical informatics where massive aggregates of data are the output
This increased data drives the development of the fast machine learning research area including extreme learning deep learning (DL) applications that experience huge growth in medical image analysis as well as other related data because of the availability of many data sets to train the DL algorithms in multimodal modes
The most used DL techniques for healthcare applications include the Autoencoder and the circumscribed Boltzmann mechanism [1]. These machine learning techniques which confirm an enhanced prospective in the learning configuration patterns and data mining features from multifarious datasets embrace the use of DL for image taxonomy and drug discoveries
Summary
Machine learning has been notably identified as less used in medical informatics where massive aggregates of data are the output This increased data drives the development of the fast machine learning research area including extreme learning deep learning (DL) applications that experience huge growth in medical image analysis as well as other related data because of the availability of many data sets to train the DL algorithms in multimodal modes. The most used DL techniques for healthcare applications include the Autoencoder and the circumscribed Boltzmann mechanism [1] These machine learning techniques which confirm an enhanced prospective in the learning configuration patterns and data mining features from multifarious datasets embrace the use of DL for image taxonomy and drug discoveries. We consider machine learning from the perspective of medical data analysis using the DL that comprehends several topics, for example, microscopic analysis of the images, ultrasonic processing, MRI analysis, denoising of medical data, CT image segmentation, slice identification, tumor detection, cell classification and segmentation, organ or vessel localization, lesion segmentation, and case detection as well as using DL mythologies such as gated recurrent units, k-support spatial pooling, pattern recognition, and multiview convolutional neural networks, combining learning with fusion
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