Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing

Recently, deep reinforcement learning, associated with medical big data generated and collected from medical Internet of ings, is prospective for computer-aided diagnosis and therapy. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. Specifically, the rapid, comprehensive, and accurate identification of pathogens is a prerequisite for clinicians to choose timely and targeted treatment. us, in this work, we present representative deep reinforcement learning methods that are potential to identify pathogens for lung infection treatment. After that, current status of pathogenic diagnosis of pulmonary infectious diseases and their main characteristics are summarized. Furthermore, we analyze the common types of secondgeneration sequencing technology, which can be used to diagnose lung infection as well. Finally, we point out the challenges and possible future research directions in integrating deep reinforcement learning with second-generation sequencing technology to diagnose and treat lung infection, which is prospective to accelerate the evolution of smart healthcare with medical Internet of ings and big data.

Section: Deep Reinforcement Learningmentioning

confidence: 99%

Section: Drl Based On Value Functionmentioning

confidence: 99%

See 1 more Smart Citation

Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection

Liu

Zhang

Zhao

et al. 2020

“…[10,11]. Zhang et al [12] proposed a Geometricconstrained multiview image matching method based on semiglobal optimization. It was obvious that some features had more information than others in a dataset.…”

Section: Introductionmentioning

confidence: 99%

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Liu

2020

Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.

“…rough the analysis of research status, we summarize three kinds of traditional MIS methods: (1) manual segmentation method, which is tedious, excessive labor, subjective, prone to error, and not suitable for large-scale research [8]; (2) semiautomatic segmentation method, which requires accurate control of prior parameters and consumes much time in the process of parameter tuning [9];…”

Section: Introductionmentioning

confidence: 99%

DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

Teng

Karim

2019

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.