A computer-aided diagnosis system for lung nodule detection in pulmonary CT images based on LS-SVM and wavelet transformation

Regions Of Interest ◽

Support Vector ◽

Cad Systems ◽

Cad System ◽

Computer Aided ◽

Spherical Structures ◽

One of the most important areas of biomedical engineering is medical imaging. Fully automated schemes are currently being explored as Computer-Aided Diagnosis (CAD) systems to provide a second opinion to medical professionals; of these systems, abnormal region detector in medical images is one of the most critical CAD systems in development. The primary motivation in using these systems is due to the fact that reading an enormous number of images is a time-consuming task for the radiologist. This task can be sped up by using a CAD system which highlights abnormal regions of interest. Low false positive rates and high sensitivity are essential requirement[s] of such a system. The initial requirement of processing any organ is an accurate segmentation of the target of interest in the images. A segmentation method based on the wavelet transformation is proposed which accurately extracts lung regions in the thoracic CT images. After this step, an Aritifical Intelligence system, known as Least Squares Support Vector Machine (LS-SVM), is employed to classify nodules within the regions of interest. It is a well known fact that the lung nodules, except the pleural nodules, are mostly spherical structures whereas other structures including blood vessels are shaped as other structures such as tubular. Therfore, an enhancment filter is developed in which spherical structures are accentuated. Processing three different real databases revealed that the proposed system has reached the objective of a CAD system to provide reliable opinion for the doctors in the diagnosis fashion.

Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrted8094.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 12261-12273

Keyword(s):

Capsule Endoscopy ◽

Current Status ◽

Human Beings ◽

Abnormality Detection ◽

Gi Tract ◽

Cad Systems ◽

Cad System ◽

Computer Aided ◽

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

Computer aided diagnosis (cad) ct images for abnormal cervix using region-based snake model and support vector machine (svm)

Journal of Physics Conference Series ◽

10.1088/1742-6596/1097/1/012007 ◽

2018 ◽

Vol 1097 ◽

pp. 012007

Author(s):

Erlinda Ratnasari Putri ◽

Prawito Prajitno ◽

Djarwani Soeharso Soejoko

Keyword(s):

Support Vector Machine ◽

Ct Images ◽

Support Vector ◽

Snake Model ◽

Computer Aided ◽

Realization methods of computer-aided diagnosis system of medical images

Recent Patents on Engineering ◽

10.2174/1872212113666191120124031 ◽

2019 ◽

Vol 13 ◽

Author(s):

Muhammad Aqeel Ashraf ◽

Shahreen Kasim

Keyword(s):

Data Model ◽

Object Segmentation ◽

Medical Images ◽

Support Vector ◽

Cad System ◽

Existing Problems ◽

Vector Machines ◽

Computer Aided ◽

: In this paper, medical images are used to realize the computer-aided diagnosis (CAD) system which develops targeted solutions to existing problems. Relying on the Mi COM platform, this system has collected and collated cases of all kinds, based on which a unified data model is constructed according to the gold standard derived by deducting each instance. Afterwards, the object segmentation algorithm is employed to segment the diseased tissues. Edge modification and feature extraction are performed for the tissue block segmented. The features extracted are classified by applying support vector machines or the Naive Bayesian classification algorithm. From the simulation results, the CAD system developed in this paper allows realization of diagnosis and treatment and sharing of data resources.

Multi-Class SVMs Based on Priority for Computer-Aided Diagnosis of Lung Nodules in CT Images

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.291-294.2742 ◽

2011 ◽

Vol 291-294 ◽

pp. 2742-2745

Author(s):

Qing Zhu Wang ◽

Xin Zhu Wang ◽

Ji Song Bie ◽

Bin Wang

Keyword(s):

Support Vector Machines ◽

Sensitivity And Specificity ◽

Ct Images ◽

Least Square ◽

Support Vector ◽

Lung Nodules ◽

Vector Machines ◽

Computer Aided ◽

A priority based ‘One against all (OAA)’ Multi-class Least Square-Support Vector Machines is designed to remove the unclassifiable regions exist in basic OAA. POAA develops the sensitivity and specificity in Computer-aided Diagnosis (CAD) for detection of lung nodules.

Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

European Thyroid Journal ◽

10.1159/000504390 ◽

2019 ◽

Vol 9 (4) ◽

pp. 186-193

Author(s):

Lei Xu ◽

Junling Gao ◽

Quan Wang ◽

Jichao Yin ◽

Pengfei Yu ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Deep Learning ◽

Diagnostic Performance ◽

Thyroid Nodules ◽

Cad Systems ◽

Cad System ◽

Computer Aided ◽

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

International Journal of Biomedical Imaging ◽

10.1155/2013/942353 ◽

2013 ◽

Vol 2013 ◽

pp. 1-46 ◽

Cited By ~ 80

Author(s):

Ayman El-Baz ◽

Garth M. Beache ◽

Georgy Gimel'farb ◽

Kenji Suzuki ◽

Kazunori Okada ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Diagnosis ◽

Clinical Importance ◽

Cad Systems ◽

Cad System ◽

Lung Cancer Diagnosis ◽

Computer Aided ◽

Aided Diagnosis ◽

Processing Steps

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effectivecomputer-aided diagnosis(CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Computer-aided diagnosis of pectus excavatum using CT images and deep learning methods

Scientific Reports ◽

10.1038/s41598-020-77361-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Lixuan Lai ◽

Siqi Cai ◽

Luyu Huang ◽

Haiyu Zhou ◽

Longhan Xie

Keyword(s):

Pectus Excavatum ◽

Learning Strategy ◽

Ct Images ◽

Fine Tuning ◽

Cad System ◽

Computer Aided ◽

The Individual ◽

Tuning Methods ◽

AbstractPectus excavatum (PE) is one of the most common chest wall defects. Accurate assessment of PE deformities is critical for effective surgical intervention. Index-based evaluations have become the standard for objectively estimating PE, however, these indexes cannot represent the whole information of chest CT images and may associated with significant error due to the individual differences. To overcome these limitations, this paper developed a computer-aided diagnosis (CAD) system based on the convolutional neural network (CNN) to automatically learn discriminative features and classify PE images. We also adopted block-wise fine-tuning methods based on the transfer learning strategy to reduce the potential risk of overfitting caused by limited data and experimentally explored the best fine-tuning degree. Our method achieved a high level of classification accuracy with 94.76% for PE diagnosis. Furthermore, we proposed a majority rule-based voting method to provide a comprehensively diagnostic results for each patient, which integrated the classification results of the whole thorax. The promising results support the feasibility of our proposed CNN-based CAD system for automatic PE diagnosis, which paves a way for comprehensive assessments of PE in clinics.

A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/450531 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 14

Author(s):

Zhiyong Pang ◽

Dongmei Zhu ◽

Dihu Chen ◽

Li Li ◽

Yuanzhi Shao

Keyword(s):

Feature Selection ◽

Level Set ◽

Support Vector ◽

Svm Classifier ◽

Segmentation Method ◽

Cad System ◽

Computer Aided ◽

Resampling Method ◽

This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer’s with Visual Support

International Journal of Neural Systems ◽

10.1142/s0129065716500507 ◽

2017 ◽

Vol 27 (03) ◽

pp. 1650050 ◽

Cited By ~ 40

Author(s):

Laila Khedher ◽

Ignacio A. Illán ◽

Juan M. Górriz ◽

Javier Ramírez ◽

Abdelbasset Brahim ◽

...

Keyword(s):

Support Vector Machine ◽

Independent Component Analysis ◽

Component Analysis ◽

Independent Component ◽

Support Vector ◽

Cad System ◽

Visual Support ◽

Computer Aided ◽

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer’s disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer’s disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.