Controlling the Behavior of a Neural Network Weights Using Variables Correlation and Posterior Probabilities Estimation

Migdady, Hazem; Talafha, Yousef; Alrabaiah, Hussam

doi:10.9790/0661-16323641

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Neural Networks are pioneering approaches in the field of machine learning, and they have a variety of applications in image processing, ranging from backpropagation neural networks to Convolutional Neural Networks (CNN) [27][28]. Exciting proofs and interpretations about the behavior of neural network weights in general, and backpropagation networks in particular, were presented by certain study [29][30]. An important study regarding the uses of neural networks in feature extraction and feature selection was presented in another paper [31].…”

Section: Literature Reviewmentioning

confidence: 99%

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

Aloudat¹,

Alomari²,

Qattous³

et al. 2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image’s region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical Center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients’ condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

Aloudat¹,

Alomari²,

Qattous³

et al. 2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…(CNN) [24]. Some research provided exciting proofs and interpretations regarding the behavior of the neural network weights in general and in the backpropagation networks in particular [25][26]. Another researches introduced a valuable study about the applications of neural networks in features extraction and features selection [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Intrahepatic Biliary Segmentation Based Image Processing Techniques using Magnetic Resonance Images

Aloudat¹,

Oudat²,

Migdady³

et al. 2020

Preprint

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A set of tubes known as bile ducts connects the liver to an organ below it directly that is called Gallbladder. The dilation of a bile duct is an important indicator regarding any serious issue in the human body. Number of reasons may cause bile duct dilation, such as: stones, tumors which commonly occur due to pancreas or papilla of vater. In this paper, the main contributions are: 1) a novel framework that consists of three phases to be applied on a set of Magnetic Resonance Imaging (MRI) images 2) an extracted set of features with their accurate values that express the condition of the biliary trees from the MRI images. Such dataset can be used in several applications to determine whether a bile duct is dilated or not. The dataset is organized as the following: half of the MRI images are for normal bile ducts, while the other half is for dilated bile ducts. To extract the useful features to diagnose the medical condition of the bile ducts from the MRI images, we implemented and applied the proposed framework that is started by using the enhanced active contour technique without edges in combination with Denoising Convolutional Neural Networks (DnCNN) to perform the segmentation and features extraction process. After that, the output of the segmentation process is the segmented biliary tree that will be used later to extract the needful features to make a diagnostic decision whether there is a dilation or not by comparing the features values of the normal versus the dilated bile ducts. We applied the feed forward neural network with backpropagation training algorithm for classification purposes. According to the experiments, the overall accuracy of the proposed framework was 90.00%. Such approach improves and increases the accuracy of the physicians’ diagnostic decisions which is considered as of significant importance for treatment and cure.

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Controlling the Behavior of a Neural Network Weights Using Variables Correlation and Posterior Probabilities Estimation

Cited by 2 publications

References 6 publications

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

Automatic Intrahepatic Biliary Segmentation Based Image Processing Techniques using Magnetic Resonance Images

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