Abstract
In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, with its uneven Healthcare imaging resources, high Healthcare imaging costs, and diagnoses often relying on doctors' clinical experience and equipment level limitations, has affected people's demand for health, so there is a need for a more accurate, convenient, and affordable Healthcare imaging system that allows all people to enjoy fair and quality Healthcare imaging services. This paper discusses the construction and evaluation of an intelligent medical diagnostic model based on integrated deep neural networks, which not only provides a systematic diagnostic analysis of the various symptoms input by the inquirer but also has higher accuracy and efficiency compared with traditional medical diagnostic models. The construction of this model provides a theoretical basis for integrating deep neural networks applied to medical neighborhoods with big data algorithms.
Highlights
Artificial neural network (ANN) is one of the research activities in the artificial intelligence neighborhood, which is a mathematical model of artificial intelligence similar to synaptic connections constructed by simulating the nervous system of a living organism, and this algorithm is like a brain composed of countless neurons, with the ability to perform large-scale operational processing, store information, and have good self-organizing learning ability, among other characteristics [1]. e emergence of neural networks has led to attempts to free computers from the mechanical execution of programs so that they can be used in a wide range of areas of life
Deep neural network (DNN), as an enhanced version of neural networks, is a neural network with many hidden layers, which is known as deep feed-forward networks (DFNs), and because neural networks are called perceptron, DNNs are known as multilayer perceptron (MLP), which can be referred to as a fully connected deep network because of the fully connected connections between its neurons [2]
It should be noted that DNN is not the name of a certain neural network, but a general term for a class of neural networks, which has a wide range of concepts and includes many categories, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are often used in applications [3] and so on, and all fall within this category
Summary
Artificial neural network (ANN) is one of the research activities in the artificial intelligence neighborhood, which is a mathematical model of artificial intelligence similar to synaptic connections constructed by simulating the nervous system of a living organism, and this algorithm is like a brain composed of countless neurons, with the ability to perform large-scale operational processing, store information, and have good self-organizing learning ability, among other characteristics [1]. e emergence of neural networks has led to attempts to free computers from the mechanical execution of programs so that they can be used in a wide range of areas of life. Based on integrated deep neural networks and making full use of medical big data and authoritative experts and medical literature [13], the construction of an artificially intelligent medical diagnosis model can make up for the deficiencies that may be brought about by experience and equipment when humans are doctors and achieve a truly interactive information-based intelligent medical platform [14], allowing the timeliness and accuracy of diagnosis to be greatly enhanced and improved. E intelligent medical diagnosis model based on integrated deep neural networks constructed in this paper can systematically evaluate and analyze the symptoms presented by patients and provide a theoretical basis for big data algorithms to prevent the occurrence of other diseases and further improve and explore the intelligent medical neighborhood Based on integrated deep neural networks and making full use of medical big data and authoritative experts and medical literature [13], the construction of an artificially intelligent medical diagnosis model can make up for the deficiencies that may be brought about by experience and equipment when humans are doctors and achieve a truly interactive information-based intelligent medical platform [14], allowing the timeliness and accuracy of diagnosis to be greatly enhanced and improved. e intelligent medical diagnosis model based on integrated deep neural networks constructed in this paper can systematically evaluate and analyze the symptoms presented by patients and provide a theoretical basis for big data algorithms to prevent the occurrence of other diseases and further improve and explore the intelligent medical neighborhood
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