scholarly journals Quantitative laryngoscopy with computer-aided diagnostic system for laryngeal lesions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chung Feng Jeffrey Kuo ◽  
Wen-Sen Lai ◽  
Jagadish Barman ◽  
Shao-Cheng Liu
Keyword(s):  
Subjective Experience ◽  
Geometric Analysis ◽  
Diagnostic System ◽  
Structural Features ◽  
Support Vector ◽  
Test Accuracy ◽  
Vocal Cords ◽  
Illumination Problem ◽  
Computer Aided ◽  
Laryngeal Lesions

AbstractLaryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

scholarly journals Quantitative Laryngoscopy With Computer-aided Diagnostic System for Laryngeal Lesions

2021 ◽  
Author(s):  
Chung-Feng Kuo ◽  
Wen-Sen Lai ◽  
Shao-Cheng Liu
Keyword(s):  
Subjective Experience ◽  
Geometric Analysis ◽  
Diagnostic System ◽  
Structural Features ◽  
Support Vector ◽  
Vocal Cords ◽  
Illumination Problem ◽  
Computer Aided ◽  
Laryngeal Lesions ◽  
Distinct Features

Abstract Background: Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria.Methods: This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. For the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range. We also proposed a method to automatically screen clear images from laryngoscopic film and automatically segment them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree.Results: This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively, and the classification accuracy for laryngeal lesions was 96.47%.Conclusion: Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

scholarly journals Computer aided diagnosis for breast cancer based on the gabor filter technique

2020 ◽  
Vol 10 (5) ◽  
pp. 5235 ◽  
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Breast Cancer ◽  
Gabor Filter ◽  
Diagnostic System ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Cancer Early Detection ◽  
False Positive Reduction ◽  
Computer Aided ◽  
Aided Diagnosis

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.

scholarly journals Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

2020 ◽  
Vol 10 (8) ◽  
pp. 2771
Author(s):  
Kwang Baek Kim ◽  
Gyeong Yun Yi ◽  
Gwang Ha Kim ◽  
Doo Heon Song ◽  
Hye Kyung Jeon
Keyword(s):  
Squamous Cell ◽  
High Speed ◽  
Morphological Characteristics ◽  
Diagnostic System ◽  
Magnifying Endoscopy ◽  
Treatment Modalities ◽  
Support Vector ◽  
Depth Of Invasion ◽  
Resonance Theory ◽  
Computer Aided

Predicting the depth of invasion of superficial esophageal squamous cell carcinomas (SESCCs) is important when selecting treatment modalities such as endoscopic or surgical resections. Recently, the Japanese Esophageal Society (JES) proposed a new simplified classification for magnifying endoscopy findings of SESCCs to predict the depth of tumor invasion based on intrapapillary capillary loops with the SESCC microvessels classified into the B1, B2, and B3 types. In this study, a four-step classification method for SESCCs is proposed. First, Niblack’s method was applied to endoscopy images to select a candidate region of microvessels. Second, the background regions were delineated from the vessel area using the high-speed fast Fourier transform and adaptive resonance theory 2 algorithm. Third, the morphological characteristics of the vessels were extracted. Based on the extracted features, the support vector machine algorithm was employed to classify the microvessels into the B1 and non-B1 types. Finally, following the automatic measurement of the microvessel caliber using the proposed method, the non-B1 types were sub-classified into the B2 and B3 types via comparisons with the caliber of the surrounding microvessels. In the experiments, 114 magnifying endoscopy images (47 B1-type, 48 B2-type, and 19 B3-type images) were used to classify the characteristics of SESCCs. The accuracy, sensitivity, and specificity of the classification into the B1 and non-B1 types were 83.3%, 74.5%, and 89.6%, respectively, while those for the classification of the B2 and B3 types in the non-B1 types were 73.1%, 73.7%, and 72.9%, respectively. The proposed machine learning based computer-aided diagnostic system could obtain the objective data by analyzing the pattern and caliber of the microvessels with acceptable performance. Further studies are necessary to carefully validate the clinical utility of the proposed system.

scholarly journals The Robust Computer Aided Diagnostic System for Lung Nodule Diagnosis

2019 ◽  
Vol 8 (4) ◽  
pp. 5670-5675 ◽  
Keyword(s):  
Lung Nodule ◽  
Diagnostic System ◽  
Computing System ◽  
Support Vector ◽  
Lung Nodules ◽  
Learning Approaches ◽  
Morphological Process ◽  
Computer Aided ◽  
Living With Cancer ◽  
The Impact

Lung cancer has been the numerous dangerous among all other variations of cancer. The fast detection of cancer is conjectured to improve the persistence rate of people living with cancer. Our objective is to present an adequate Computer-Aided Diagnosis (CAD) for the identification of lung nodules from the parenchyma area of the lung and yield the nodule into except cancerous or non-cancerous. In this suggestion, A new Hybrid Classifier method has been Prescribed to detect lung nodules based on numerous image processing and machine learning approaches. The construction of this hybrid system is the combination of unsupervised Enhanced Fuzzy C-Mean (EFCM) clustering and Weighted supervised support vector machine (WSVM). The suggested process includes the subsequent operations: i) the image used is magnified originally. Then the area of concern is cropped, where the user can choose the area to be cropped. ii) The morphological process is implemented to overcome the blood vessels and magnify the nodules. iii)Nodules are distinguished by labeling.iv)Those classified nodule's characteristics are obtained.v)Neural networks are performed as the classifiers that work primarily based on the features chosen. And also, this proposed flexible computing system was associated with the various well-known learning Techniques. The WSVM for analysis is exhibited in this paper, where the execution of misclassification for each practice sample is unusual. The proposed work was capable of detecting the lung nodule that appears near the lung wall. The Provisional results intimate that the recommended method defeats the impact of outliers and yields higher classification speed than previous algorithms.

scholarly journals Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Fang Yang ◽  
Murat Hamit ◽  
Chuan B. Yan ◽  
Juan Yao ◽  
Abdugheni Kutluk ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Feature Extraction ◽  
Median Filter ◽  
Characteristic Curve ◽  
Diagnostic System ◽  
Classification Performance ◽  
Image Features ◽  
Support Vector ◽  
X Ray ◽  
Computer Aided

Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine andK-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

COMPUTER-AIDED DIAGNOSTIC SYSTEM FOR FEATURE-BASED CLASSIFICATION USING HEART RATE VARIABILITY

2020 ◽  
Vol 32 (02) ◽  
pp. 2050009
Author(s):  
Kirti Tripath ◽  
Harsh Sohal ◽  
Shruti Jain
Keyword(s):  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Diagnostic System ◽  
Support Vector ◽  
Ecg Signal ◽  
K Nearest Neighbor ◽  
Computer Aided ◽  
Feature Based

This article proposes a computer-aided diagnostic system for feature-based selection classification (CAD-FSC) to detect arrhythmia, atrial fibrillation and normal sinus rhythm. The CAD-FSC methodology encompasses of ECG signal processing phases: ECG pre-processing, R-peak detection, feature extraction, feature selection and ECG classification. Digital filters are used to pre-process the ECG signal and the R-peak is detected by using the Pan-Tompkin’s algorithm. The heart rate variability (HRV) features are extracted in time and frequency domains. Among them, the prominent features are selected with analysis of variance (ANOVA) using Statistical Package for the Social Sciences (SPSS) tool. Cubic support vector machine (C-SVM), coarse Gaussian support vector machine (CG-SVM), cubic k-nearest neighbor (C-kNN) and weighted k-nearest neighbor (W-kNN) classifiers are utilized to validate the CAD-FSC system for three-stage classification. The C-SVM outperforms all other classifiers by giving higher overall accuracy of 98.4% after feature selection of time domain and frequency domain.

scholarly journals Computer-Aided Diagnostic System for Digital Mammography

2021 ◽  
Vol 9 (2) ◽  
pp. 989-995
Author(s):  
Yang Xingyao, Muhammad Fayaz, Tanmay Ghosh, Khan Alamgir
Keyword(s):  
Nearest Neighbor ◽  
Diagnostic System ◽  
Principal Component ◽  
Window Size ◽  
Selection Method ◽  
Support Vector ◽  
Features Selection ◽  
Forward Selection ◽  
Cad Systems ◽  
Computer Aided

In this work, Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems are developed and tested using the public and freely available mammographic databases named MIAS and DDSM databases, respectively. CADe system is used to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing a breast abnormality. At the same time, CADx is developed to distinguish between normal, benign and malignant breast tissues, and it helps radiologists to decide whether a biopsy is needed when reading a diagnostic mammogram or not. Any CAD system is constituted of typical stages including preprocessing and segmentation of mammogram images, extraction of regions of interest (ROI), features removal, features selection and classification. In both proposed CAD systems, ROIs are selected using a window size of 32×32 pixels, then a total of 543 features from four different feature categories are extracted from each ROI and then normalized. After that, the selection of the most relevant features is performed using four different selection methods from MATLAB Pattern Recognition Toolbox v.5 (PRtool5) named Sequential Backward Selection (SBS), Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS) and Branch and Bound Selection (BBS) methods. We also utilized Principal Component Analysis (PCA) as the fifth method to reduce the dimensions of the features set. After that, we used different classifiers such as Support Vector Machines (SVM), K-voting Nearest Neighbor (K-NN), Quadratic Discriminant Analysis (QDA) and Artificial Neural Networks (ANN) for the classification. Both CAD systems have the same implementation stages but different output. CADe systems are designed to detect breast abnormalities while CADx system indicates the likelihood of malignancy of lesions. Finally, we independently compared the performance of all classifiers with each selection method in both modes. The evaluation of the proposed CAD systems is done using performance indices such as sensitivity, specificity, the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curves, the overall accuracy and Cohen-k factor. Both CAD systems provided encouraging results. These results were different corresponding to the selection method and classifier.

Observer Variation in Clinical Diagnosis - A Computer-Aided Assessment of its Magnitude and Importance in 552 Patients with Abdominal Pain

1973 ◽  
Vol 12 (02) ◽  
pp. 108-113 ◽  
Author(s):  
P. W. Gill ◽  
D. J. Leaper ◽  
P. J. Guillou ◽  
J. R. Staniland ◽  
J. C. Horhocks ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Clinical Diagnosis ◽  
Error Rate ◽  
Diagnostic System ◽  
Diagnostic Error ◽  
Observer Variation ◽  
History Taking ◽  
Computer Aided ◽  
Computer Based ◽  
Initial Survey

This report describes an evaluation of »observer variation« in history taking and examination of patients with abdominal pain. After an initial survey in which the degree of observer variation amongst the present authors fully confirmed previous rather gloomy forecasts, a system of »agreed definitions« was produced, and further studies showed a rapid and considerable fall in the degree of observer variation between the data recorded by the same authors. Finally, experience with a computer-based diagnostic system using the same system of agreed definitions showed the maximum diagnostic error rate due to faulty acquisition of data to be low (4.7°/o in a series of 552 cases). It is suggested as a result of these studies that — at least in respect of abdominal pain — errors in data acquisition by the clinician need not be the prime cause of faulty diagnoses.

Similarity detection of English text and teaching evaluation based on improved TCUSS clustering algorithm

2020 ◽  
pp. 1-11
Author(s):  
Yu Wang
Keyword(s):  
Language Processing ◽  
Calculation Method ◽  
Clustering Algorithm ◽  
Research Result ◽  
Structural Features ◽  
English Text ◽  
Support Vector ◽  
Similarity Calculation ◽  
Other Information ◽  
Calculation Task

The semantic similarity calculation task of English text has important influence on other fields of natural language processing and has high research value and application prospect. At present, research on the similarity calculation of short texts has achieved good results, but the research result on long text sets is still poor. This paper proposes a similarity calculation method that combines planar features with structured features and uses support vector regression models. Moreover, this paper uses PST and PDT to represent the syntax, semantics and other information of the text. In addition, through the two structural features suitable for text similarity calculation, this paper proposes a similarity calculation method combining structural features with Tree-LSTM model. Experiments show that this method provides a new idea for interest network extraction.

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