Classification of mammographic masses: comparison between Backpropagation Neural Network (BNN) and human readers

2003 ◽  
Author(s):  
Lina Arbach ◽  
Darus L. Bennett ◽  
Joseph M. Reinhardt ◽  
Ghassan Fallouh
Keyword(s):  
Neural Network ◽  
Backpropagation Neural Network

Classification of Road Damage from Digital Image Using Backpropagation Neural Network

2017 ◽  
Vol 6 (4) ◽  
pp. 159
Author(s):  
Sutikno Sutikno ◽  
Helmie Arif Wibawa ◽  
Prima Yusuf Budiarto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Digital Image ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Detection System ◽  
Backpropagation Neural Network ◽  
Accuracy Rate ◽  
Neural Network Algorithm

One of the biggest causes of death in the world is a traffic accident. Road damage is one of the cause factors from the traffic accident. To reduce this problem is required an early detection against road damage. This paper describes how to classify road damage using image processing and backpropagation neural network. Image processing is used to obtain binary image consists of a normalization, grayscaling, edge detection and thresholding, while the backpropagation neural network algorithm is used for classifying. The conclusion of this test that the algorithm is able to provide the accuracy rate of 83%. The results of this research may contribute to the development of road damage detection system based on the digital image so that the traffic accidents caused by road damage can be reduced.

scholarly journals Classification of Tropical Rain Forest East Kalimantan Using Image Processing and Backpropagation Neural Network Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2586-2589
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
Backpropagation Neural Network ◽  
East Kalimantan ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
High Level

Tropical Rain Forest located in East Kalimantan has a high level of biodiversity, with a high level of biodiversity in east kalimantan then it needs a method to classify the existing plants there. In the research, the researchers tried to classify 5 plants found in tropical rainforests, namely ShoreaBalangeran, Dryobalanopsbeccarii Dyer, Eusideroxylonzwageri, Duriokutejensis, Cerberamanghas. Classification is done by using backpropagation neural network algorithm combined with image processing, where the image used is the image of plant leaf. The result of this research is the classification of 5 species of this plant with precision value above 90% in order to become a supporter of botanical decision in determining the type of plant and become alternative reference to classify plants in tropical rain forest area.

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