Automatic classification of Malaria Using Artificial Neural Network

2019 ◽  
Vol 1 (1) ◽  
pp. 20-24
Author(s):  
Khawla Bashier ◽  
Nashwa Osman ◽  
Wafaa Suliman ◽  
Sarah Musaa ◽  
Eslam Attia
Keyword(s):  
Neural Network ◽  
Graphical User Interface ◽  
Test Method ◽  
Textural Features ◽  
Plasmodium Parasite ◽  
First Order ◽  
Show Result ◽  
Design Systems ◽  
The Tropics

Malaria is one of the three most serious diseases worldwide, affecting 225 million infections each year in Sudan, mainly in the tropics where the most serious illnesses are caused by Plasmodium parasite. Automatic diagnosis design systems have been implemented to detect the presence of two types of Malaria (falciparum, vivax) using neural network. Firstly, the data has been acquisition from website and laboratory. The images were filtered to remove the noise using morphological filter, in order to separate the parasite from the other cell in the image k-means method is carried out. Then features (statics first order) were selected from textural features by t-test method, and neural network has been used to classify two types of Malaria. Finally, a graphical user interface has been designed to show result for two types of Malaria. After Complete the designing 95.45% accuracy 90.9%, sensitivity and 100% specificity has been determined.

Identification and Classification of Three Iranian Rice Varieties in Mixed Bulks Using Image Processing and MLP Neural Network

2017 ◽  
Vol 13 (5) ◽  
Author(s):  
Saeideh Fayyazi ◽  
Mohammad Hossein Abbaspour-Fard ◽  
Abbas Rohani ◽  
S. Amirhassan Monadjemi ◽  
Hassan Sadrnia
Keyword(s):  
Neural Network ◽  
Economic Value ◽  
Principal Component ◽  
Textural Features ◽  
Training Algorithms ◽  
Rice Varieties ◽  
Mlp Neural Network ◽  
Pca Method ◽  
Rice Seeds

Abstract Due to variation in economic value of different varieties of rice, reports indicating the possibility of mixing different varieties on the market. Applying machine vision techniques to classify rice varieties is a method which can increase the accuracy of classification process in real applications. In this study, several morphological and textural features of rice seeds’ images were examined to evaluate their efficacy in identification of three Iranian rice varieties (Tarom, Fajr, Shiroodi) in their mixed samples. On the whole, 666 images of rice seeds (222 images of each variety) were acquired at a stable illumination condition and totally, 17 morphological and 41 textural features were extracted from seeds images. Principal component analysis (PCA) method was employed to select and rank the most significant features for the classification. Subsequently, the MLP neural network classifier was employed for classification of rice varieties in the mixed bulks of three and two varieties, using top selected features. The network was three-layered feed forward type and trained using two training algorithms (BB and BDLRF). The classification accuracy of 55.93, 84.62 and 82.86 % for Fajr, Tarom and Shiroodi, 86.96 and 93.02 % for Fajr and Shiroodi, 86.84 and 96.08 % for Tarom and Shiroodi and 91.49 and 95.24 % for Fajr and Tarom were obtained in test phase, respectively.

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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