�ݺ�ߣshows by User: farzadfarahani

�ݺ�ߣshows by User: farzadfarahani / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: farzadfarahani / Mon, 26 Oct 2015 12:14:42 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: farzadfarahani Lung nodule diagnosis from CT images based on ensemble learning /slideshow/lung-nodule-diagnosis-from-ct-images-based-on-ensemble-learning/54381080 cibcbpresentation-151026121442-lva1-app6892
Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.]]>
Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.]]> Mon, 26 Oct 2015 12:14:42 GMT /slideshow/lung-nodule-diagnosis-from-ct-images-based-on-ensemble-learning/54381080 farzadfarahani@slideshare.net(farzadfarahani) Lung nodule diagnosis from CT images based on ensemble learning farzadfarahani Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cibcbpresentation-151026121442-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Early detection of cancer is the most promising way to enhance a patient's chance for survival. This paper presents a computer-aided classification method using computed tomography (CT) images of the lung based on ensemble of three classifiers including MLP, KNN and SVM. In this study, the entire lung is first segmented from the CT images and specific features like Roundness, Circularity, Compactness, Ellipticity, and Eccentricity are calculated from the segmented images. These morphological features are used for classification process in a way that each classifier makes its own decision. Finally, majority voting method is used to combine decisions of this ensemble system. The performance of this system is evaluated using 60 CT scans collected by Lung Image Database Consortium (LIDC) and the results show good improvement in diagnosing of pulmonary nodules.

Lung nodule diagnosis from CT images based on ensemble learning from Farzad Vasheghani Farahani

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0 Fuzzy rule based expert system for diagnosis of lung cancer /farzadfarahani/fuzzy-rule-based-expert-system-for-diagnosis-of-lung-cancer nafipspresentation-151026114824-lva1-app6892
Lung cancer is the second most common cancer in both men and women in the world. The focus of this paper is to design a fuzzy rule-based medical expert system for diagnosis of lung cancer. The proposed system consists of four modules: working memory, knowledge base, inference engine and user interface. The system takes the risk factors and symptoms of lung cancer in a two-step process and stores them as facts of the problem in working memory. Also domain expert knowledge is gathered to generate rules and stored in the rule base. The rule base consists of two different rule sets related to risk factors and symptoms of lung cancer respectively. Finally, type-2 fuzzy inference engine fires relevant rules under appropriate condition and provides the probability of disease as output of the system. The output of the system could act as a second opinion to assist the physicians. Also graphical user interface is presented to facilitate the communication between user and system.]]>
Lung cancer is the second most common cancer in both men and women in the world. The focus of this paper is to design a fuzzy rule-based medical expert system for diagnosis of lung cancer. The proposed system consists of four modules: working memory, knowledge base, inference engine and user interface. The system takes the risk factors and symptoms of lung cancer in a two-step process and stores them as facts of the problem in working memory. Also domain expert knowledge is gathered to generate rules and stored in the rule base. The rule base consists of two different rule sets related to risk factors and symptoms of lung cancer respectively. Finally, type-2 fuzzy inference engine fires relevant rules under appropriate condition and provides the probability of disease as output of the system. The output of the system could act as a second opinion to assist the physicians. Also graphical user interface is presented to facilitate the communication between user and system.]]> Mon, 26 Oct 2015 11:48:24 GMT /farzadfarahani/fuzzy-rule-based-expert-system-for-diagnosis-of-lung-cancer farzadfarahani@slideshare.net(farzadfarahani) Fuzzy rule based expert system for diagnosis of lung cancer farzadfarahani Lung cancer is the second most common cancer in both men and women in the world. The focus of this paper is to design a fuzzy rule-based medical expert system for diagnosis of lung cancer. The proposed system consists of four modules: working memory, knowledge base, inference engine and user interface. The system takes the risk factors and symptoms of lung cancer in a two-step process and stores them as facts of the problem in working memory. Also domain expert knowledge is gathered to generate rules and stored in the rule base. The rule base consists of two different rule sets related to risk factors and symptoms of lung cancer respectively. Finally, type-2 fuzzy inference engine fires relevant rules under appropriate condition and provides the probability of disease as output of the system. The output of the system could act as a second opinion to assist the physicians. Also graphical user interface is presented to facilitate the communication between user and system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nafipspresentation-151026114824-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Lung cancer is the second most common cancer in both men and women in the world. The focus of this paper is to design a fuzzy rule-based medical expert system for diagnosis of lung cancer. The proposed system consists of four modules: working memory, knowledge base, inference engine and user interface. The system takes the risk factors and symptoms of lung cancer in a two-step process and stores them as facts of the problem in working memory. Also domain expert knowledge is gathered to generate rules and stored in the rule base. The rule base consists of two different rule sets related to risk factors and symptoms of lung cancer respectively. Finally, type-2 fuzzy inference engine fires relevant rules under appropriate condition and provides the probability of disease as output of the system. The output of the system could act as a second opinion to assist the physicians. Also graphical user interface is presented to facilitate the communication between user and system.

Fuzzy rule based expert system for diagnosis of lung cancer from Farzad Vasheghani Farahani

]]> 380 5 https://cdn.slidesharecdn.com/ss_thumbnails/nafipspresentation-151026114824-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Value Engineering /slideshow/value-engineering-42414994/42414994 valueengineering-141206000637-conversion-gate02
This presentation is about Value Engineering and contains: 1.History of VE 2.Value Concept 3.What is Value Engineering? 4.Implementation of VE in our project 5.Principle and Purpose of VE 6.Case Study 7.Conclusion]]>
This presentation is about Value Engineering and contains: 1.History of VE 2.Value Concept 3.What is Value Engineering? 4.Implementation of VE in our project 5.Principle and Purpose of VE 6.Case Study 7.Conclusion]]> Sat, 06 Dec 2014 00:06:37 GMT /slideshow/value-engineering-42414994/42414994 farzadfarahani@slideshare.net(farzadfarahani) Value Engineering farzadfarahani This presentation is about Value Engineering and contains: 1.History of VE 2.Value Concept 3.What is Value Engineering? 4.Implementation of VE in our project 5.Principle and Purpose of VE 6.Case Study 7.Conclusion <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/valueengineering-141206000637-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This presentation is about Value Engineering and contains: 1.History of VE 2.Value Concept 3.What is Value Engineering? 4.Implementation of VE in our project 5.Principle and Purpose of VE 6.Case Study 7.Conclusion

Value Engineering from Farzad Vasheghani Farahani

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0 Multiple Classifier Systems /slideshow/multiple-classifier-systems-41198001/41198001 multipleclassifiersystems-141106040422-conversion-gate01
This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers.]]>
This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers.]]> Thu, 06 Nov 2014 04:04:22 GMT /slideshow/multiple-classifier-systems-41198001/41198001 farzadfarahani@slideshare.net(farzadfarahani) Multiple Classifier Systems farzadfarahani This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/multipleclassifiersystems-141106040422-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers.

Multiple Classifier Systems from Farzad Vasheghani Farahani

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0 https://cdn.slidesharecdn.com/profile-photo-farzadfarahani-48x48.jpg?cb=1571089035 http://cil.aut.ac.ir/wp-content/uploads/2014/12/Farzad-Vasheghani.pdf https://cdn.slidesharecdn.com/ss_thumbnails/cibcbpresentation-151026121442-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/lung-nodule-diagnosis-from-ct-images-based-on-ensemble-learning/54381080 Lung nodule diagnosis ... https://cdn.slidesharecdn.com/ss_thumbnails/nafipspresentation-151026114824-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds farzadfarahani/fuzzy-rule-based-expert-system-for-diagnosis-of-lung-cancer Fuzzy rule based exper... https://cdn.slidesharecdn.com/ss_thumbnails/valueengineering-141206000637-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/value-engineering-42414994/42414994 Value Engineering