Colorectal Polyp Detection Using Feedforward Neural Network with Image Feature Selection

2018 ◽  
Author(s):  
Arif Wirawan Muhammad ◽  
Ginanjar Wiro Sasmito ◽  
Imam Riadi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Colorectal Polyp ◽  
Feedforward Neural Network ◽  
Image Feature ◽  
Polyp Detection

Calvarial eosinophilic granuloma: Diagnostic models and image feature selection with a neural network

1998 ◽  
Vol 5 (6) ◽  
pp. 427-434 ◽  
Author(s):  
Estanislao Arana ◽  
Luis Martí-Bonmatí ◽  
Daniel Bautista ◽  
Roberto Paredes
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Eosinophilic Granuloma ◽  
Image Feature ◽  
Diagnostic Models

Assistance system for real-time polyp detection based on convolutional neural network

2021 ◽  
Vol 75 (6) ◽  
pp. 540-543
Author(s):  
Daniel Kvak ◽  
Karolína Kvaková
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Detection Method ◽  
Spatial Location ◽  
Colorectal Polyp ◽  
Machine Learning Algorithms ◽  
Polyp Detection ◽  
Effi Ciency ◽  
Computer Aided

Summary: The use of artifi cial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years. Machine learning algorithms promise to improve the effi ciency of polyp detection and even optical localization of fi ndings, all with minimal training of the endoscopist. The practical goal of this study is to analyse the CAD software (computer-aided dia gnosis) Carebot for colorectal polyp detection using a convolutional neural network. The proposed binary classifier for polyp detection achieves accuracy of up to 98%, specifi city of 0.99 and precision of 0.96. At the same time, the need for the availability of large-scale clinical data for the development of artifi cial- -intelligence-based models for the automatic detection of adenomas and benign neoplastic lesions is discussed. Key words: polyp detection – convolutional neural network – artifi cial intelligence – computer-aided dia gnosis – spatial location

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

Carpal Bone Segmentation Using Fully Convolutional Neural Network

2019 ◽  
Vol 15 (10) ◽  
pp. 983-989
Author(s):  
Liang Kim Meng ◽  
Azira Khalil ◽  
Muhamad Hanif Ahmad Nizar ◽  
Maryam Kamarun Nisham ◽  
Belinda Pingguan-Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Bone Age ◽  
Image Feature ◽  
Qualitative Assessment ◽  
Carpal Bone ◽  
Radiographic Images ◽  
Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

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