Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network

1998 ◽  
Vol 8 (3) ◽  
pp. 261-266 ◽  
Author(s):  
M Binder ◽  
H Kittler ◽  
A Seeber ◽  
A Steiner ◽  
H Pehamberger ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Analysis ◽  
Skin Lesions ◽  
Computerized Image Analysis ◽  
Pigmented Skin Lesions ◽  
Artificial Neural

Classification of Boiled Shrimp's Shape Using Image Analysis and Artificial Neural Network Model

2014 ◽  
Vol 37 (3) ◽  
pp. 257-263 ◽  
Author(s):  
Poonpat Poonnoy ◽  
Panupong Yodkeaw ◽  
Akkarin Sriwai ◽  
Pongpol Umongkol ◽  
Saowanit Intamoon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural

scholarly journals A PRELIMARY APPROACH FOR THE AUTOMATED RECOGNITION OF MALIGNANT MELANOMA

2011 ◽  
Vol 23 (2) ◽  
pp. 121 ◽  
Author(s):  
Ezzeddine Zagrouba ◽  
Walid Barhoumi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Color Image ◽  
Skin Lesions ◽  
Benign Lesions ◽  
Data Set ◽  
Pigmented Lesions ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural

In this work, we are motivated by the desire to classify skin lesions as malignants or benigns from color photographic slides of the lesions. Thus, we use color images of skin lesions, image processing techniques and artificial neural network classifier to distinguish melanoma from benign pigmented lesions. As the first step of the data set analysis, a preprocessing sequence is implemented to remove noise and undesired structures from the color image. Second, an automated segmentation approach localizes suspicious lesion regions by region growing after a preliminary step based on fuzzy sets. Then, we rely on quantitative image analysis to measure a series of candidate attributes hoped to contain enough information to differentiate melanomas from benign lesions. At last, the selected features are supplied to an artificial neural network for classification of tumor lesion as malignant or benign. For a preliminary balanced training/testing set, our approach is able to obtain 79.1% of correct classification of malignant and benign lesions on real skin lesion images.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Sign in / Sign up

Export Citation Format

Share Document