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

2013 ◽  
Vol 2013 ◽  
pp. 1-46 ◽  
Author(s):  
Ayman El-Baz ◽  
Garth M. Beache ◽  
Georgy Gimel'farb ◽  
Kenji Suzuki ◽  
Kazunori Okada ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Clinical Importance ◽  
Computer Aided Diagnosis ◽  
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.

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

2019 ◽  
Vol 8 (4) ◽  
pp. 12261-12273
Keyword(s):  
Capsule Endoscopy ◽  
Computer Aided Diagnosis ◽  
Current Status ◽  
Human Beings ◽  
Abnormality Detection ◽  
Gi Tract ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

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.

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

2021 ◽  
Author(s):  
Omid Talakoub
Keyword(s):  
Wavelet Transformation ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Regions Of Interest ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Spherical Structures ◽  
Aided Diagnosis

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.

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

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 ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

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.

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

2021 ◽  
Author(s):  
Omid Talakoub
Keyword(s):  
Wavelet Transformation ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Regions Of Interest ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Spherical Structures ◽  
Aided Diagnosis

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.

scholarly journals Computer System for Perception of Lung Cancer

2019 ◽  
Vol 8 (12S) ◽  
pp. 868-872
Keyword(s):  
Lung Cancer ◽  
Survival Rate ◽  
Lung Tumor ◽  
Mean Square ◽  
Cad Systems ◽  
Cad System ◽  
Implementation Purpose ◽  
Computer Aided ◽  
Diagnosis Model ◽  
Aided Diagnosis

Detection of cancer in its previous stages to increases survival rate of patient. CAD system is efficient because it take minimum time to detect weather the patient has cancer or not. It is very difficult for detection of lung cancer its earlier stage as it takes many tests. There are many of the CAD system which is designed for earlier detection of tumors. Many CAD systems have been designed in past for early detection of lung tumor. For segmentation purpose Thresholding is used and detection of area in which suspected tumor part growing algorithms is used. There are various factor is calculated using GLCM. Multilayer feed forward BPNN approaches for classify the feature set. Performance is calculated in form of mean square error (MSE) using BPNN. The CAD (computer aided diagnosis) model gives 90% true count. For implementation purpose MATLAB is used.

Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided
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