scholarly journals Microarray spot partitioning by autonoumsly organising maps thorugh contour model

2020 ◽  
Vol 10 (1) ◽  
pp. 746 ◽  
Author(s):  
Karthik S. A. ◽  
Manjunath S. S.
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Experimental Work ◽  
Microarray Image ◽  
Test Spot ◽  
Contour Model ◽  
Comprehensive Information ◽  
Microarray Spot

In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization.

scholarly journals Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

2021 ◽  
Vol 733 (1) ◽  
pp. 012005
Author(s):  
Y Hendrawan ◽  
R Utami ◽  
D Y Nurseta ◽  
Daisy ◽  
S Nuryani ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chili Pepper ◽  
Capsicum Frutescens

scholarly journals Artificial neural networks and computer image analysis in the evaluation of selected quality parameters of pea seeds

2019 ◽  
Vol 132 ◽  
pp. 01027 ◽  
Author(s):  
Katarzyna Szwedziak
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Computer Image ◽  
Good Method ◽  
Quality Parameters ◽  
Computer Image Analysis ◽  
Pea Seeds ◽  
Artificial Neural

The aim of the study was to develop an innovative method of modelling the process of evaluating the quality of agricultural crops on the basis of computer image analysis and artificial neural networks (ANN). It was therefore assumed that on the basis of the prepared application for processing and analysing the acquired digital images, based on the RGB colour recognition model, a quick and good method of assessing the quality of products would be obtained. An experiment was conducted on the evaluation of selected parameters of pea seeds quality using computer image analysis and the obtained results were verified by artificial neural networks using the geostatic function.

CLASSIFYING APPLES BY THE MEANS OF FLUORESCENCE IMAGING

2004 ◽  
Vol 18 (02) ◽  
pp. 157-174 ◽  
Author(s):  
MARIUS C. CODREA ◽  
OLLI S. NEVALAINEN ◽  
ESA TYYSTJÄRVI ◽  
MARTIN VANDEVEN ◽  
ROLAND VALCKE
Keyword(s):  
Neural Networks ◽  
Fluorescence Imaging ◽  
Chlorophyll A ◽  
Blue Light ◽  
Chlorophyll A Fluorescence ◽  
Classification Method ◽  
Test Material ◽  
Fluorescence Images

Classification of harvested apples when predicting their storage potential is an important task. This paper describes how chlorophyll a fluorescence images taken in blue light through a red filter, can be used to classify apples. In such an image, fluorescence appears as a relatively homogenous area broken by a number of small nonfluorescing spots, corresponding to normal corky tissue patches, lenticells, and to damaged areas that lower the quality of the apple. The damaged regions appear more longish, curved or boat-shaped compared to the roundish, regular lenticells. We propose an apple classification method that employs a hierarchy of two neural networks. The first network classifies each spot according to geometrical criteria and the second network uses this information together with global attributes to classify the apple. The system reached 95% accuracy using a test material classified by an expert for "bad" and "good" apples.

Automatic Segmentation and Classification of Outdoor Images Using Neural Networks

1997 ◽  
Vol 08 (01) ◽  
pp. 137-144 ◽  
Author(s):  
N. W. Campbell ◽  
B. T. Thomas ◽  
T. Troscianko
Keyword(s):  
Neural Networks ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Multi Layer Perceptron ◽  
Feature Map ◽  
Segmentation Process ◽  
Image Area

The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perceptron is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.

Recognition of Heartbeat Categories Applying a Novel Preprocessing Scheme and Neural Networks

2020 ◽  
Author(s):  
Andres Hernandez-Matamoros ◽  
Hamido Fujita ◽  
Hector Perez-Meana
Keyword(s):  
Quality Of Life ◽  
Neural Networks ◽  
Heart Disease ◽  
P Wave ◽  
Correct Classification ◽  
Major Contributor ◽  
Automated Diagnosis ◽  
Ecg Signals

Heart disease is the principal cause of mortality and the major contributor to reduced quality of life. The electrocardiogram is used to monitor the cardiovascular system. The correct classification of the beats in electrocardiograms gives an opportunity to have treatment more focused. The manual analysis of the ECG signals faces different problems. For this reason, automated diagnosis systems are fed by ECG signals to detect anomalies. In this paper, we propose a method based on a novel preprocessing approach and neural networks for the classification of heartbeats which is able to classify five categories of arrhythmias in accordance with the AAMI standard. The preprocessing stage allows each beat to have “P wave-R peak-R peak” information. We evaluated the proposed method on the MIT-BIH database, which is one of the most used databases. According to the results, the proposed approach is able to make predictions with the average accuracies of 97%. The average accuracies are compared to different approaches that use different preprocessing and classifier stages. Our approach is superior to that of most of them.

A Combinational Fuzzy Clustering Approach for Microarray Spot Segmentation

2017 ◽  
pp. 326-344
Author(s):  
Ong Pauline ◽  
Zarita Zainuddin
Keyword(s):  
Image Analysis ◽  
Performance Assessment ◽  
Fuzzy Clustering ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Microarray Experiment ◽  
Shape Detection ◽  
Microarray Image ◽  
Clustering Approach ◽  
Microarray Spot

Due to microarray experiment imperfection, spots with various artifacts are often found in microarray image. A more rigorous spot recognition approach in ensuring successful image analysis is crucial. In this paper, a novel hybrid algorithm was proposed. A wavelet approach was applied, along with an intensity-based shape detection simultaneously to locate the contour of the microarray spots. The proposed algorithm segmented all the imperfect spots accurately. Performance assessment with the classical methods, i.e., the fixed circle, adaptive circle, adaptive shape and histogram segmentation showed that the proposed hybrid approach outperformed these methods.

Image Analysis for cDNA Microarrays

2005 ◽  
Vol 44 (03) ◽  
pp. 405-407 ◽  
Author(s):  
J. Rahnenführer
Keyword(s):  
Image Analysis ◽  
Information Loss ◽  
Intensity Histogram ◽  
Background Estimation ◽  
Detection Algorithms ◽  
Microarray Image ◽  
Hybrid Approaches ◽  
Algorithmic Approaches ◽  
Array Hybridization

Summary Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. Methods: We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections. Results: For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection algorithms, intensity histogram methods and hybrid approaches have been proposed. The correctness of final intensity estimates is difficult to verify. Nevertheless, the application of sophisticated algorithms provides a significant reduction of the possible information loss. Conclusions: The initial analysis step for array hybridization experiments is the estimation of expression intensities. The quality of this process is crucial for the validity of interpretations from subsequent analysis steps.

scholarly journals Evaluation of scratch and pre-trained convolutional neural networks for the classification of Tomato plant diseases

2021 ◽  
Vol 10 (2) ◽  
pp. 467
Author(s):  
Mohammad Amimul Ihsan Aquil ◽  
Wan Hussain Wan Ishak
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tomato Plant ◽  
State Of The Art ◽  
Plant Diseases ◽  
Fine Tuning ◽  
Convolutional Network ◽  
Tomato Diseases

<span id="docs-internal-guid-01580d49-7fff-6f2a-70d1-7893ec0a6e14"><span>Plant diseases are a major cause of destruction and death of most plants and especially trees. However, with the help of early detection, this issue can be solved and treated appropriately. A timely and accurate diagnosis is critical in maintaining the quality of crops. Recent innovations in the field of deep learning (DL), especially in convolutional neural networks (CNNs) have achieved great breakthroughs across different applications such as the classification of plant diseases. This study aims to evaluate scratch and pre-trained CNNs in the classification of tomato plant diseases by comparing some of the state-of-the-art architectures including densely connected convolutional network (Densenet) 120, residual network (ResNet) 101, ResNet 50, ReseNet 30, ResNet 18, squeezenet and Vgg.net. The comparison was then evaluated using a multiclass statistical analysis based on the F-Score, specificity, sensitivity, precision, and accuracy. The dataset used for the experiments was drawn from 9 classes of tomato diseases and a healthy class from PlantVillage. The findings show that the pretrained Densenet-120 performed excellently with 99.68% precision, 99.84% F-1 score, and 99.81% accuracy, which is higher compared to its non-trained based model showing the effectiveness of using a combination of a CNN model with fine-tuning adjustment in classifying crop diseases.</span></span>

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