Neural Network Based Automated System for Diagnosis of Cervical Cancer

2015 ◽  
Vol 4 (2) ◽  
pp. 26-39 ◽  
Author(s):  
Seema Singh ◽  
V. Tejaswini ◽  
Rishya P. Murthy ◽  
Amit Mutgi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cervical Cancer ◽  
Back Propagation ◽  
Accurate Method ◽  
Automated System ◽  
Low Grade ◽  
Back Propagation Algorithm ◽  
The Neural Network ◽  
Processing Techniques

Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology images that are used for discriminating various stages of cervical cancer. The dominant features used for diagnosis are Nucleus to cytoplasm ratio, shape, and color intensity along with nucleus area, perimeter and eccentricity. These features are used to train the neural network using Back-propagation algorithm of supervised training method. The cytology cells were then successfully classified as non-cancerous, low- grade and high-grade cancer cells.

Neural Network Based Automated System for Diagnosis of Cervical Cancer

2020 ◽  
pp. 1422-1436
Author(s):  
Seema Singh ◽  
V. Tejaswini ◽  
Rishya P. Murthy ◽  
Amit Mutgi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cervical Cancer ◽  
Back Propagation ◽  
Accurate Method ◽  
Automated System ◽  
Low Grade ◽  
Back Propagation Algorithm ◽  
The Neural Network ◽  
Processing Techniques

Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology images that are used for discriminating various stages of cervical cancer. The dominant features used for diagnosis are Nucleus to cytoplasm ratio, shape, and color intensity along with nucleus area, perimeter and eccentricity. These features are used to train the neural network using Back-propagation algorithm of supervised training method. The cytology cells were then successfully classified as non-cancerous, low- grade and high-grade cancer cells.

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

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.

scholarly journals Breast Cancer Detection using Image Processing Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 391-399 ◽  
Author(s):  
Prannoy Giri ◽  
K. Saravanakumar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Back Propagation ◽  
Texture Features ◽  
Screening Mammography ◽  
Breast Cancers ◽  
Back Propagation Algorithm ◽  
Digital Database ◽  
Processing Techniques ◽  
Aided Diagnosis

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it’s infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

Application of neural networks in industrial production

2021 ◽  
pp. 10-17
Author(s):  
S. S. Yudachev ◽  
N. A. Gordienko ◽  
F. M. Bosy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Effective Means ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Graphics Processors ◽  
Training Scheme ◽  
Network Training ◽  
The Neural Network

The article describes an algorithm for the synthesis of neural networks for controlling the gyrostabilizer. The neural network acts as an observer of the state vector. The role of such an observer is to provide feedback to the gyrostabilizer, which is illustrated in the article. Gyrostabilizer is a gyroscopic device designed to stabilize individual objects or devices, as well as to determine the angular deviations of objects. Gyrostabilizer systems will be more widely used, as they provide an effective means of motion control with a number of significant advantages for various designs. The article deals in detail with the issue of specific stage features of classical algorithms: selecting the network architecture, training the neural network, and verifying the results of feedback control. In recent years, neural networks have become an increasingly powerful tool in scientific computing. The universal approximation theorem states that a neural network can be constructed to approximate any given continuous function with the required accuracy. The back propagation algorithm also allows effectively optimizing the parameters when training a neural network. Due to the use of graphics processors, it is possible to perform efficient calculations for scientific and engineering tasks. The article presents the optimal configuration of the neural network, such as the depth of memory, the number of layers and neurons in these layers, as well as the functions of the activation layer. In addition, it provides data on dynamic systems to improve neural network training. An optimal training scheme is also provided.

LEAF DISEASE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 77 (17) ◽  
Author(s):  
Syafiqah Ishak ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Hashim Saad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Back Propagation ◽  
Disease Classification ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Leaf Quality ◽  
Leaf Disease ◽  
Color Transformation

Nowadays, herb plants are importance to medical field and can give benefit to human. In this research, Phyllanthus Elegans Wall (Asin-Asin Gajah) is used to analyse and to classify whether it is healthy or unhealthy leaf. This plant was chosen because its function can cure breast cancer. Therefore, there is a need for alternative cure for patient of breast cancer rather than use the technology such as Chemotherapy, surgery or use of medicine from hospital. The purpose of this research to identify the quality of leaf and using technology in agriculture field. The process to analysis the leaf quality start from image acquisition, image processing, and classification. For image processing method, the most important for this part is the segmentation using HSV to input RGB image for the color transformation structure. The analysis of leaf disease image is applied based on colour and shape. Finally, the classification method use feed-forward Neural Network, which uses Back-propagation algorithm. The result shows comparison between Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) and comparison between MLP and RBF shown in percentage of accuracy. MLP and RBF is algorithm for Neural Network. Conclusively, classifier of Neural Network shows better performance and more accuracy.

scholarly journals A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

2012 ◽  
pp. 56-61
Author(s):  
Pratibha Rani ◽  
Anshu Sirohi ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Network System ◽  
Morphological Operations ◽  
Gray Level ◽  
Back Propagation Algorithm ◽  
Translation Invariant ◽  
The Neural Network ◽  
Neural Network System ◽  
Level Shifts

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.

A Neural Network-Based Prediction of the Weld Lines in Resin Transfer Molded Parts

1999 ◽  
Author(s):  
Faezeh Soltani ◽  
Souran Manoochehri
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Simulation Models ◽  
Optimization Methods ◽  
Computational Time ◽  
Back Propagation Algorithm ◽  
Rtm Process ◽  
The Neural Network ◽  
Molded Parts ◽  
The Neural Networks

Abstract A model is developed to predict the weld lines in Resin Transfer Molding (RTM) process. In this model, the preforms are assumed to be thin flat with isotropic and orthotropic permeabilities. The position of the weld lines formed by multiple specified inlet ports are predicted using a neural network-based back propagation algorithm. The neural network was trained with data obtained from simulation and actual molding experimentation. Part geometry is decomposed into smaller sections based on the position of the weld lines. The variety of preforms and processing conditions are used to verify the model. Applying the neural networks reduced the amount of computational time by several orders of magnitude compared with simulations. The models developed in this study can be effectively utilized in iterative optimization methods where use of numerical simulation models is cumbersome.

Modeling of Cell Deformation Under External Force Using Neural Network

2010 ◽  
Author(s):  
M. T. Ahmadian ◽  
G. R. Vossoughi ◽  
A. A. Abbasi ◽  
P. Raeissi
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Point Load ◽  
Cell Deformation ◽  
Back Propagation Algorithm ◽  
Three Dimensional Model ◽  
Error Back Propagation ◽  
Injection Process ◽  
The Neural Network

Embryogenesis, regeneration and cell differentiation in microbiological entities are influenced by mechanical forces. Therefore, development of mechanical properties of these materials is important. Neural network technique is a useful method which can be used to obtain cell deformation by the means of force-geometric deformation data or vice versa. Prior to insertion in the needle injection process, deformation and geometry of cell under external point-load is a key element to understand the interaction between cell and needle. In this paper the goal is the prediction of cell membrane deformation under a certain force, and to visually estimate the force of indentation on the membrane from membrane geometries. The neural network input and output parameters are associated to a three dimensional model without the assumption of the adherent affects. The neural network is modeled by applying error back propagation algorithm. In order to validate the strength of the developed neural network model, the results are compared with the experimental data on mouse oocyte and mouse embryos that are captured from literature. The results of the modeling match nicely the experimental findings.

Application of Neural Network for the Prediction of Tensile Properties of Friction Stir Welded Composites

2016 ◽  
Vol 880 ◽  
pp. 128-131 ◽  
Author(s):  
Arun Kumar Shettigar ◽  
Subramanya Prabhu ◽  
Rashmi Malghan ◽  
Shrikantha Rao ◽  
Mervin Herbert
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Base Material ◽  
Welding Process ◽  
Weld Strength ◽  
Back Propagation Algorithm ◽  
Friction Stir ◽  
Error Back Propagation ◽  
The Neural Network ◽  
Tool Pin

In this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error.

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