scholarly journals Colonoscopy Image Pre-Processing for the Development of Computer-Aided Diagnostic Tools

10.5772/67842 ◽  
2018 ◽  
Author(s):  
Alain Sánchez-González ◽  
Begoña García-Zapirain Soto
Keyword(s):  
Diagnostic Tools ◽  
Computer Aided

scholarly journals Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess

2011 ◽  
Vol 2 (1) ◽  
pp. 25 ◽  
Author(s):  
UlyssesJ Balis ◽  
Jason Hipp ◽  
Thomas Flotte ◽  
James Monaco ◽  
Jerome Cheng ◽  
...  
Keyword(s):  
Lessons Learned ◽  
Diagnostic Tools ◽  
Computer Aided

scholarly journals Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ebenezer Owusu ◽  
Prince Boakye-Sekyerehene ◽  
Justice Kwame Appati ◽  
Julius Yaw Ludu
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Missing Values ◽  
Disease Risk ◽  
Heart Diseases ◽  
Cleveland Clinic ◽  
Diagnostic Tools ◽  
Support Vector ◽  
Computer Aided ◽  
Heart Disease Risk

Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels. The datasets which contain 13 attributes such as gender, age, blood pressure, and chest pain are taken from the Cleveland clinic. In total, there were 303 records with 6 tuples having missing values. To clean the data, we deleted the 6 missing records through the listwise technique. The size of data, and the fact that it is a purely random subset, made this approach have no significant effect for the experiment because there were no biases. Salient features are selected using the boosting technique to speed up and improve accuracies. Using the train/test split approach, the data is then partitioned into training and testing. SVM is then used to train and test the data. The C parameter is set at 0.05 and the linear kernel function is used. Logistic regression, Nave Bayes, decision trees, Multilayer Perceptron, and random forest were used to compare the results. The proposed boosting SVM performed exceptionally well, making it a better tool than the existing techniques.

scholarly journals Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects

2017 ◽  
Vol 2 (3) ◽  
pp. 245-249
Author(s):  
Andrei-Constantin Ioanovici ◽  
Andrei-Marian Feier ◽  
Ioan Țilea ◽  
Daniela Dobru
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Computational Models ◽  
Positive Impact ◽  
Screening Method ◽  
Colorectal Polyps ◽  
Machine Learning Algorithms ◽  
Diagnostic Tools ◽  
Computer Aided ◽  
Important Health

Abstract Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.

scholarly journals Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

2021 ◽  
Vol 7 (2) ◽  
pp. 879-882
Author(s):  
Elmer Jeto Gomes Ataide ◽  
Shubham Agrawal ◽  
Aishwarya Jauhari ◽  
Axel Boese ◽  
Alfredol Illanes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Thyroid Nodules ◽  
Performance Metrics ◽  
Diagnostic System ◽  
Diagnostic Tools ◽  
Observer Variability ◽  
Dice Coefficient ◽  
Computer Aided

Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability

scholarly journals Ada-WHIPS: Explaining AdaBoost Classification with Applications in the Health Sciences

2020 ◽  
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R.M. Atif Azad
Keyword(s):  
Machine Learning ◽  
Statistical Tests ◽  
Black Box ◽  
Classification Rule ◽  
Diagnostic Tools ◽  
Rule Based ◽  
Medical Practitioners ◽  
Research Areas ◽  
Computer Aided ◽  
Novel Algorithm

Abstract Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML) models and high dimensional data sources (electronic health records, MRI scans, cardiotocograms, etc). These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years, because it addresses the interpretability and trust concerns of medical practitioners and other critical decision makers. Method In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge -- to explain AdaBoost classification -- with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, \textit{Adaptive-Weighted High Importance Path Snippets} (Ada-WHIPS), makes use of AdaBoost's adaptive classifier weights; using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes at the internals of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model's decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure \textit{stability} that is better suited to the XAI setting. Results In this paper, our experimental results demonstrate the benefits of using our novel algorithm for explaining AdaBoost classification. The simple rule-based explanations have better generalisation (mean coverage 15\%-68\%) while remaining competitive for specificity (mean precision 80\%-99\%). A very small trade-off in specificity is shown to guard against over-fitting. Conclusions This research demonstrates that interpretable, classification rule-based explanations can be generated for computer aided diagnostic tools based on AdaBoost, and that a tightly coupled, AdaBoost-specific approach can outperform model-agnostic methods.

scholarly journals Ada-WHIPS: Explaining AdaBoost Classification with Applications in the Health Sciences

2019 ◽  
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R.M. Atif Azad
Keyword(s):  
Machine Learning ◽  
Statistical Tests ◽  
Black Box ◽  
Classification Rule ◽  
Diagnostic Tools ◽  
Rule Based ◽  
Medical Practitioners ◽  
Research Areas ◽  
Computer Aided ◽  
Novel Algorithm

Abstract Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients' disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning (ML) models and high dimensional data sources (electronic health records, MRI scans, cardiotocograms, etc). These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years, because it addresses the interpretability and trust concerns of medical practitioners and other critical decision makers. Method In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge -- to explain AdaBoost classification -- with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, \textit{Adaptive-Weighted High Importance Path Snippets} (Ada-WHIPS), makes use of AdaBoost's adaptive classifier weights; using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes at the internals of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model's decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure \textit{stability} that is better suited to the XAI setting. Results In this paper, our experimental results demonstrate the benefits of using our novel algorithm for explaining AdaBoost classification. The simple rule-based explanations have better generalisation (mean coverage 15\%-68\%) while remaining competitive for specificity (mean precision 80\%-99\%). A very small trade-off in specificity is shown to guard against over-fitting. Conclusions This research demonstrates that interpretable, classification rule-based explanations can be generated for computer aided diagnostic tools based on AdaBoost, and that a tightly coupled, AdaBoost-specific approach can outperform model-agnostic methods.

A Computer Aided Diagnostic Tool for the Detection of Uterine Fibroids

2013 ◽  
Vol 2 (1) ◽  
pp. 26-38
Author(s):  
N. Sriraam ◽  
L. Vinodashri
Keyword(s):  
Neural Network ◽  
Standard Deviation ◽  
Uterine Fibroid ◽  
Diagnostic Tool ◽  
Probabilistic Neural Network ◽  
Medical Community ◽  
Diagnostic Tools ◽  
Morphological Operations ◽  
Statistical Features ◽  
Computer Aided

The integration of information technology with biomedicine has provided viable diagnostic tools to the medical community. Such computer aided procedures fastens the clinical decision process without any hurdle. Among different medical imaging modalities, Ultrasonic Imaging plays a vital role in detecting gynecological pathologies. Of importance, Uterine fibroid detection requires significant attention where symptoms such as, infertility and miscarriage can be predicted. This paper suggests an automated computer aided diagnostic tool for the detection of uterine fibroid. Gabor wavelets are applied for texture segmentation and statistical features such as mean, variance, standard deviation, skewness, kurtosis, Eigen values, GLCM contrast and energy are extracted from the user defined region of interest (ROI). The qualitative procedure is examined using the morphological operations and gray level intensity variations. Two neural network models, multilayer perceptron neural network (MLP) and probabilistic neural network (PNN) are applied to classify the normal and fibroid uterus image. It is found from the experimental computer simulation, a classification accuracy of 97.25% is obtained using combinational statistical features, mean and standard deviation with PNN classifier. It can be concluded that the proposed tool can applied as an efficient Medical Expert System for diagnosing the Ultrasonic Uterus images.

Computer-aided methods for 3-D visualization of serial sections and thick biological specimens

1992 ◽  
Vol 50 (2) ◽  
pp. 1060-1061
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Three Dimensional ◽  
Neuritic Plaque ◽  
Dimensional Structure ◽  
Data Sets ◽  
Molecular Physiology ◽  
Research Activities ◽  
Computer Aided ◽  
Dimensional Reconstruction

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.

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