A sensory-neural network for medical diagnosis

2017 ◽  
Author(s):  
Mihael Sok ◽  
Eva Svegl ◽  
Igor Grabec
Keyword(s):  
Neural Network ◽  
Medical Diagnosis ◽  
Sensory Neural

Other Diseases Detection

2014 ◽  
pp. 170-180
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Intelligent System ◽  
Associated Symptoms ◽  
Artificial Neural ◽  
Inputs And Outputs

Considering the importance of the problem of medical diagnosis, this chapter investigates the application of an intelligent system based on artificial neural network for decision making for Hepatitis. First, datasets are provided for detecting Hepatitis, based on the requirements of artificial neural network inputs and outputs consisting of associated symptoms of each disease as fields of patients' records. Then multilayer perceptron (MLP) artificial neural network is trained to classify Hepatitis disease. In the next sections, details are described.

Application of an Adaptive Hybrid Neural Network to Medical Diagnosis

2000 ◽  
pp. 329-336
Author(s):  
Chee Peng Lim ◽  
Poh Suan Teoh ◽  
Phaik Yean Goay ◽  
Robert F. Harrison
Keyword(s):  
Neural Network ◽  
Medical Diagnosis ◽  
Hybrid Neural Network

scholarly journals Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

2020 ◽  
Vol 96 ◽  
pp. 106691 ◽  
Author(s):  
Gonçalo Marques ◽  
Deevyankar Agarwal ◽  
Isabel de la Torre Díez
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Diagnosis

Neural Network Technologies in Medical Diagnosis (Review)

2018 ◽  
pp. 284-294
Author(s):  
Mariya V. Vyucheyskaya ◽  
◽  
Irina N. Kraynova ◽  
Anatoliy V. Gribanov
Keyword(s):  
Neural Network ◽  
Medical Diagnosis ◽  
Network Technologies ◽  
Neural Network Technologies

scholarly journals A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jing Zhao ◽  
Lo-Yi Lin ◽  
Chih-Min Lin
Keyword(s):  
Neural Network ◽  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Intuitionistic Fuzzy Sets ◽  
The Novel ◽  
Score Functions ◽  
Medical Diagnoses ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Linguistic ◽  
Cerebellar Model Neural Network

The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN). To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs) and intuitionistic fuzzy cross-entropy (IFCE) with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories.

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

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