Microscopic Image Recognition-based Fish Disease Diagnosis System

2011 ◽  
Vol 6 (10) ◽  
pp. 355-364 ◽  
Author(s):  
SangWoong Lee ◽  
JeongSeon Park
Keyword(s):  
Image Recognition ◽  
Disease Diagnosis ◽  
Microscopic Image ◽  
Fish Disease ◽  
Diagnosis System

scholarly journals An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Derya Avci ◽  
Akif Dogantekin
Keyword(s):  
Genetic Algorithm ◽  
Parkinson Disease ◽  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Disease Diagnosis ◽  
Major Public Health Problem ◽  
Diagnosis System ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Hidden Neurons

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

scholarly journals An in-field automatic wheat disease diagnosis system

2017 ◽  
Vol 142 ◽  
pp. 369-379 ◽  
Author(s):  
Jiang Lu ◽  
Jie Hu ◽  
Guannan Zhao ◽  
Fenghua Mei ◽  
Changshui Zhang
Keyword(s):  
Disease Diagnosis ◽  
Diagnosis System

Microscopic Image Recognition by Medical Multi-agent Systems

2019 ◽  
pp. 403-416
Author(s):  
Tatiana Istomina ◽  
Viktor Istomin ◽  
Elena Petrunina ◽  
Anatoliy Nikolskiy ◽  
Aleksandr Beloglazov
Keyword(s):  
Image Recognition ◽  
Microscopic Image ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

Heart Disease Diagnosis System Using Fuzzy Logic

2018 ◽  
Author(s):  
Hasan Kahtan ◽  
Kamal Z. Zamli ◽  
Wan Nor Ashikin Wan Ahmad Fatthi ◽  
Azma Abdullah ◽  
Mansoor Abdulleteef ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Diagnosis System ◽  
Heart Disease Diagnosis

Neuro-Imaging Machine Learning Techniques for Alzheimer's Disease Diagnosis

2021 ◽  
pp. 191-209
Author(s):  
Gehad Ismail Sayed ◽  
Aboul Ella Hassanien
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Bench Mark ◽  
Learning Tools ◽  
Diagnosis System ◽  
Neuro Imaging ◽  
Medical Imagining

Alzheimer's disease (AD) is considered one of the most common dementia's forms affecting senior's age staring from 65 and over. The standard method for identifying AD are usually based on behavioral, neuropsychological and cognitive tests and sometimes followed by a brain scan. Advanced medical imagining modalities such as MRI and pattern recognition techniques are became good tools for predicting AD. In this chapter, an automatic AD diagnosis system from MRI images based on using machine learning tools is proposed. A bench mark dataset is used to evaluate the performance of the proposed system. The adopted dataset consists of 20 patients for each diagnosis case including cognitive impairment, Alzheimer's disease and normal. Several evaluation measurements are used to evaluate the robustness of the proposed diagnosis system. The experimental results reveal the good performance of the proposed system.

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