scholarly journals Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sang Hoon Kim ◽  
Youngbae Hwang ◽  
Dong Jun Oh ◽  
Ji Hyung Nam ◽  
Ki Bae Kim ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Test Results ◽  
Promising Candidate ◽  
Reading Methods ◽  
Validation Set ◽  
Wireless Capsule

AbstractThe manual reading of capsule endoscopy (CE) videos in small bowel disease diagnosis is time-intensive. Algorithms introduced to automate this process are premature for real clinical applications, and multi-diagnosis using these methods has not been sufficiently validated. Therefore, we developed a practical binary classification model, which selectively identifies clinically meaningful images including inflamed mucosa, atypical vascularity or bleeding, and tested it with unseen cases. Four hundred thousand CE images were randomly selected from 84 cases in which 240,000 images were used to train the algorithm to categorize images binarily. The remaining images were utilized for validation and internal testing. The algorithm was externally tested with 256,591 unseen images. The diagnostic accuracy of the trained model applied to the validation set was 98.067%. In contrast, the accuracy of the model when applied to a dataset provided by an independent hospital that did not participate during training was 85.470%. The area under the curve (AUC) was 0.922. Our model showed excellent internal test results, and the misreadings were slightly increased when the model was tested in unseen external cases while the classified ‘insignificant’ images contain ambiguous substances. Once this limitation is solved, the proposed CNN-based binary classification will be a promising candidate for developing clinically-ready computer-aided reading methods.

scholarly journals Efficacy of a Comprehensive Binary Classification Model Using a Deep Convolutional Neural Network for Wireless Capsule Endoscopy

2020 ◽  
Author(s):  
Sang Hoon Kim ◽  
Youngbae Hwang ◽  
Dong Jun Oh ◽  
Ji Hyung Nam ◽  
Ki Bae Kim ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Wireless Capsule Endoscopy ◽  
Classification Model ◽  
Test Results ◽  
Reading Methods ◽  
Bowel Diseases ◽  
Validation Set ◽  
Wireless Capsule

Abstract Manual reading of capsule endoscopy (CE) video is a time-consuming process in diagnosing small bowel diseases. Although many algorithms have been introduced, multi-diagnosis has not been sufficiently validated. They are promising but still premature to be used in clinical practice. Therefore, we developed a practical binary classification model and tested it with unseen cases.400,000 CE images were randomly selected from 84 cases. Among them, 240,000 were used to train an algorithm to categorize images binarily. The remaining images were utilized for validation and internal testing. The algorithm was externally tested with 256,591 unseen images.Diagnostic accuracy was 98.067% when the trained model was applied to the validation set. It was 97.946% when applied to images for internal testing. When the model was applied to a dataset provided by an independent hospital not participated during training, its accuracy was 85.470%. The area under the curve was 0.922.Our binary classification model showed excellent internal test results, and when tested in unseen external cases, misreadings were slightly increased while judging ‘insignificant’ images containing ambiguous substances. When we can get over this problem, CNN-based binary classification will become the most promising candidates for developing clinically ready computer-aided reading methods.

scholarly journals An Efficient Gastrointestinal Hemorrhage Detection and Diagnosis Model for Wireless Capsule Endoscopy

2019 ◽  
Vol 8 (3) ◽  
pp. 7549-7554 ◽  
Keyword(s):  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Classification Model ◽  
Support Vector ◽  
Frequency Spectra ◽  
Kernel Support Vector Machine ◽  
Detection And Diagnosis ◽  
New Feature ◽  
Occurrence Matrix ◽  
Wireless Capsule

Wireless Capsule Endoscopy (WCE) captures the section of human gastrointestinal (GI) tract which is impossible by the classical endoscopy investigations. A main limitation exist in the method is the requirement of analyzing massive data quantity for detecting the diseases which consumes more time and increases the burden to the physicians. As a result, there is a requirement to effectively develop an automated model to detect and diagnosis diseases on the WCEimages. The design of the presented model depends upon the examination of the patterns exist in frequency spectra of the WCE frames because of the occurrence of bleeding regions. For the exploration of the discriminating patterns,this study presents a new feature extraction based classification model is developed. An efficient Normalized Gray Level Co-occurrence Matrix (NGLCM) is applied for extracting the features of the GI images. Then, a kernel support vector machine (KSVM) with particle swarm optimization (PSO) is applied for the classification of the processed GI images. The experimentation takes place on the benchmark GI images to verify the superior nature of the presented model. The results confirmed the enhanced classifier outcome of the presented model on all the applied images under several aspects

Computer-aided diagnosis of gastrointestinal ulcer and hemorrhage using wireless capsule endoscopy: a systematic review and diagnostic test accuracy meta-analysis (Preprint)

2021 ◽  
Author(s):  
Chang Seok Bang ◽  
Jae Jun Lee ◽  
Gwang Ho Baik
Keyword(s):  
Diagnostic Test ◽  
Capsule Endoscopy ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Test Accuracy ◽  
Test Accuracy ◽  
Cad Models ◽  
Sensitivity Specificity ◽  
Wireless Capsule

BACKGROUND Interpretation of capsule endoscopy images or movies is operator-dependent and time-consuming. As a result, computer-aided diagnosis (CAD) has been applied to enhance the efficacy and accuracy of the review process. Two previous meta-analyses reported the diagnostic performance of CAD models for gastrointestinal ulcers or hemorrhage in capsule endoscopy. However, insufficient systematic reviews have been conducted, which cannot determine the real diagnostic validity of CAD models. OBJECTIVE To evaluate the diagnostic test accuracy of CAD models for gastrointestinal ulcers or hemorrhage using wireless capsule endoscopic images. METHODS We conducted core databases searching for studies based on CAD models for the diagnosis of ulcers or hemorrhage using capsule endoscopy and presenting data on diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS Overall, 39 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of ulcers (or erosions) were .97 (95% confidence interval, .95–.98), .93 (.89–.95), .92 (.89–.94), and 138 (79–243), respectively. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of hemorrhage (or angioectasia) were .99 (.98–.99), .96 (.94–0.97), .97 (.95–.99), and 888 (343–2303), respectively. Subgroup analyses showed robust results. Meta-regression showed that published year, number of training images, and target disease (ulcers vs. erosions, hemorrhage vs. angioectasia) was found to be the source of heterogeneity. No publication bias was detected. CONCLUSIONS CAD models showed high performance for the optical diagnosis of gastrointestinal ulcer and hemorrhage in wireless capsule endoscopy. CLINICALTRIAL International Prospective Register of Systematic Reviews (PROSPERO): CRD42021253454 ; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=42021253454.

scholarly journals Effective Search of Triterpenes with Anti-HSV-1 Activity Using a Classification Model by Logistic Regression

2021 ◽  
Vol 9 ◽  
Author(s):  
Keiko Ogawa ◽  
Seikou Nakamura ◽  
Haruka Oguri ◽  
Kaori Ryu ◽  
Taichi Yoneda ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Natural Products ◽  
Predictive Model ◽  
Binary Classification ◽  
External Validation ◽  
Area Under The Curve ◽  
Classification Model ◽  
Active Compounds ◽  
Logistic Regression Algorithm ◽  
Hsv 1

Natural products are an excellent source of skeletons for medicinal seeds. Triterpenes and saponins are representative natural products that exhibit anti-herpes simplex virus type 1 (HSV-1) activity. However, there has been a lack of comprehensive information on the anti-HSV-1 activity of triterpenes. Therefore, expanding information on the anti-HSV-1 activity of triterpenes and improving the efficiency of their exploration are urgently required. To improve the efficiency of the development of anti-HSV-1 active compounds, we constructed a predictive model for the anti-HSV-1 activity of triterpenes by using the information obtained from previous studies using machine learning methods. In this study, we constructed a binary classification model (i.e., active or inactive) using a logistic regression algorithm. As a result of the evaluation of predictive model, the accuracy for the test data is 0.79, and the area under the curve (AUC) is 0.86. Additionally, to enrich the information on the anti-HSV-1 activity of triterpenes, a plaque reduction assay was performed on 20 triterpenes. As a result, chikusetsusaponin IVa (11: IC50 = 13.06 μM) was found to have potent anti-HSV-1 with three potentially anti-HSV-1 active triterpenes. The assay result was further used for external validation of predictive model. The prediction of the test compounds in the activity test showed a high accuracy (0.83) and AUC (0.81). We also found that this predictive model was found to be able to successfully narrow down the active compounds. This study provides more information on the anti-HSV-1 activity of triterpenes. Moreover, the predictive model can improve the efficiency of the development of active triterpenes by integrating many previous studies to clarify potential relationships.

A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-22
Author(s):  
Mazin Abed Mohammed ◽  
Mohamed Elhoseny ◽  
Karrar Hameed Abdulkareem ◽  
Salama A. Mostafa ◽  
Mashael S. Maashi
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Test Results ◽  
Multi Agent ◽  
The Voice

Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.

scholarly journals A Convolutional Neural Network Based Disease Diagnosis in Wireless Capsule Endoscopy Images

2019 ◽  
Vol 9 (1) ◽  
pp. 327-331
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Operating Characteristic ◽  
Color Space ◽  
Disease Diagnosis ◽  
Wireless Capsule Endoscopy ◽  
Classifier Design ◽  
Multiple Abnormalities ◽  
Wireless Capsule

In wireless capsule endoscopy (WCE), a swallowable miniature optical endoscope is used to transmit color images of the gastrointestinal tract. However, the number of images transmitted is large, taking a significant amount of the medical expert’s time to review the scan. In this research, we propose a technique to automate the abnormality detection in WCE images. We split the image into several patches and extract features pertaining to each block using a Convolutional neural network (CNN) to increase their generality while overcoming the drawbacks of manually crafted features. We intend to exploit the importance of color information for the task. Experiments are performed to determine the optimal color space components for feature extraction and classifier design. We obtained an area under receiver-operating-characteristic (ROC) curve of approximately 0.8 on a dataset containing multiple abnormalities

scholarly journals Sediment Level Prediction of a Combined Sewer System Using Spatial Features

2021 ◽  
Vol 13 (7) ◽  
pp. 4013
Author(s):  
Marc Ribalta ◽  
Carles Mateu ◽  
Ramon Bejar ◽  
Edgar Rubión ◽  
Lluís Echeverria ◽  
...  
Keyword(s):  
Binary Classification ◽  
Area Under The Curve ◽  
Classification Model ◽  
Gradient Boosting ◽  
Accuracy Score ◽  
Short Term ◽  
Sewer System ◽  
Spatial Features ◽  
Combined Sewer ◽  
Combined Sewer System

The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909.

Wireless Capsule Endoscopy – Uses Beyond GI Bleeding?

Endoscopy ◽  
2006 ◽  
Vol 38 (11) ◽  
Author(s):  
P McConville ◽  
WJ Cash ◽  
RGP Watson ◽  
JS Collins
Keyword(s):  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Gi Bleeding ◽  
Wireless Capsule
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