scholarly journals Cancer Clumps Detection using Image Processing Based on Cell Counting and Artificial Neural Network Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 5124-5126 ◽  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Cancer Cell ◽  
Feedback System ◽  
Area Measurement ◽  
Cell Counting ◽  
Medical Sciences ◽  
Artificial Neural ◽  
Flood Fill

Cancer is one of the main reasons for death among humans. So much research has been done for detecting and diagnosing cancer using image processing and classification and techniques. But the disease remains as one of the deadeist disease. Thus early detection of the disease is only one of the reasons to cure the cancer. In this proposed technique identifying cancer cell by using Image Processing, Artificial Neural Network techniques using cell counting, area measurement and detection of clumps. With the help of proposed technique we detect the cancer traits of any CT image, mammography image of biopsy samples automatically. So many algorithms was proposed but there was a lack of flexibility and the level of accuracy is not consists. Before applying proposed algorithm, the system preprocesses the input images with various techniques like gray scaling, binarization, inversion and flood fill operation. The proposed method can be work on various images and fine tuned with a feedback system and if can effectively used for automatically detection of cancer cells in a unique way and lead to open up new dimension in detecting cancer cell in the field of medical sciences.

Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

2016 ◽  
Vol 231 (6) ◽  
pp. 1206-1216 ◽  
Author(s):  
Mustafa Ayyıldız ◽  
Kerim Çetinkaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Geometric Shapes ◽  
Testing Data ◽  
Artificial Neural

In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

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