Fish Disease Diagnosis System Based on Image Processing of Pathogens' Microscopic Images

2007 ◽  
Author(s):  
Jeong-Seon Park ◽  
Myung-Joo Oh ◽  
Soonhee Han
Keyword(s):  
Image Processing ◽  
Disease Diagnosis ◽  
Fish Disease ◽  
Diagnosis System ◽  
Microscopic Images

scholarly journals Detection of Cotton Leaf Disease Using Image Processing Techniques

2021 ◽  
Vol 2062 (1) ◽  
pp. 012009
Author(s):  
Sushreeta Tripathy
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Region Of Interest ◽  
Texture Features ◽  
Disease Diagnosis ◽  
Cotton Leaf ◽  
Diagnosis System ◽  
Image Processing Techniques ◽  
Plant Disease Diagnosis ◽  
Processing Techniques

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

scholarly journals Skin Disease Diagnosis System using Image Processing and Data Mining

2018 ◽  
Vol 179 (16) ◽  
pp. 38-40
Author(s):  
R. S. ◽  
Priyanka S. ◽  
Jyoti B. ◽  
Priyanka K.
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Skin Disease ◽  
Disease Diagnosis ◽  
Diagnosis System

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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

scholarly journals A Hybrid Methodology for Automation the Diagnosis of Leukemia Based on Quantitative and Morphological Feature Analysis

2018 ◽  
Vol 12 (3) ◽  
pp. 56 ◽  
Author(s):  
Hussam N. Fakhouri ◽  
Saleh H. Al-Sharaeh
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
White Blood Cells ◽  
Medical Image Processing ◽  
Initial Diagnosis ◽  
Feature Analysis ◽  
Microscopic Images ◽  
Different Types ◽  
The Status ◽  
Blast Cells

Recent year’s witnessed a huge revolution for developing an automated diagnosis for different disease such as cancer using medical image processing. Many researches have been dedicated to achieve this goal. Analyzing medical microscopic histology images provide us with large information about the status of patient and the progress of diseases, help to determine if the tissue have any pathological changes. Automation of the diagnosis of these images will lead to better, faster and enhanced diagnosis for different hematological and histological tissue images such as cancer. This paper propose an automated methodology for analyzing cancer histology and hematology microscopic images to detect leukemia using image processing by combining two diagnosis procedures initial and advance; the initial diagnosis depend on the percentage of the white blood cells in microscopic images affected by leukemia as indicator for the existence of leukemia in the blood smear sample. Whereas, the advance diagnosis classifying the leukemia according into different types using feature bag classifier. The experimental results showed that the proposed methodology initial diagnosis is able to detect leukemia images and differentiate it from samples that do not have leukemia. While, advance diagnosis it is able to detect and classify most leukemia types and differentiate between acute and chronic, but in some cases in the chronic leukemia where the percent of blast cells and shape are similar; it gave a diagnosis of the type of leukemia to the most similar type.

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