Babar Anwar | SHOBHIT UNIVERSITY, MEERUT, ; UPTU(JPIET) MEERUT (original) (raw)
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Papers by Babar Anwar
In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) ... more In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Genetic Algorithms (GA) and morphological techniques. First of all, GA has been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then features extraction have been performed. Finally SVM have been used to classify ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset.
In Pattern recognition, ensembles of classifiers are used to increase the performance and accurac... more In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy Clustering and Fuzzy Entropy (FCFE) based classification model. With the help of FCM we obtained fuzzy membership matrix, revealing the underlying distribution and structure of the data. The Fuzzy entropy tells us about the degree of difficulty of classification of data. This information is used in sampling the training data into core sample and boundary sample. This sampling approach induces diversity in the ensemble which contributes to higher classification accuracy. The proposed method is evaluated on 4 UCI benchmark data sets with support vector machine (SVM) as the base classifier. The decision is combined using mean combiner rule. The results show that the proposed method delivers higher classification accuracy than stand alone SVM and the well known ensembles techniques of Bagging and Boosting.
In this paper, a fuzzy based impulse noise removal technique has been proposed. The proposed filt... more In this paper, a fuzzy based impulse noise removal technique has been proposed. The proposed filter is based on noise detection, fuzzy set construction, histogram estimation and fuzzy filtering process. Noise detection process is used to identify the set of noisy pixels which are used for estimating the histogram of the original image. Estimated histogram of the original image is used for fuzzy set construction using fuzzy number construction algorithm. Fuzzy filtering process is the main component of the proposed technique. It consists of fuzzification, defuzzification and predicted intensity processes to remove impulse noise. Sensitivity analysis of the proposed technique has been performed by varying the number of fuzzy sets. Experimental results demonstrate that the proposed technique achieves much better performance than state-of-the-art filters. The comparison of the results is based on global error measure as well as local error measures i.e. mean square error (MSE) and structural similarity index measure (SSIM).
Abstract: Mobile Ad hoc networks (MANET) are of dynamic and ever changing topologies which demand... more Abstract: Mobile Ad hoc networks (MANET) are of dynamic and ever changing topologies which demands, a new approach to design and develop an efficient routing strategy. There exists lot of routing protocols proposed so far in the literature. These protocols can broadly ...
Medical Principles and Practice, 2011
In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) ... more In this paper we have proposed a method for lungs nodule detection from computed tomography (CT) scanned images by using Genetic Algorithms (GA) and morphological techniques. First of all, GA has been used for automated segmentation of lungs. Region of interests (ROIs) have been extracted by using 8 directional searches slice by slice and then features extraction have been performed. Finally SVM have been used to classify ROI that contain nodule. The proposed system is capable to perform fully automatic segmentation and nodule detection from CT Scan Lungs images. The technique was tested against the 50 datasets of different patients received from Aga Khan Medical University, Pakistan and Lung Image Database Consortium (LIDC) dataset.
In Pattern recognition, ensembles of classifiers are used to increase the performance and accurac... more In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy Clustering and Fuzzy Entropy (FCFE) based classification model. With the help of FCM we obtained fuzzy membership matrix, revealing the underlying distribution and structure of the data. The Fuzzy entropy tells us about the degree of difficulty of classification of data. This information is used in sampling the training data into core sample and boundary sample. This sampling approach induces diversity in the ensemble which contributes to higher classification accuracy. The proposed method is evaluated on 4 UCI benchmark data sets with support vector machine (SVM) as the base classifier. The decision is combined using mean combiner rule. The results show that the proposed method delivers higher classification accuracy than stand alone SVM and the well known ensembles techniques of Bagging and Boosting.
In this paper, a fuzzy based impulse noise removal technique has been proposed. The proposed filt... more In this paper, a fuzzy based impulse noise removal technique has been proposed. The proposed filter is based on noise detection, fuzzy set construction, histogram estimation and fuzzy filtering process. Noise detection process is used to identify the set of noisy pixels which are used for estimating the histogram of the original image. Estimated histogram of the original image is used for fuzzy set construction using fuzzy number construction algorithm. Fuzzy filtering process is the main component of the proposed technique. It consists of fuzzification, defuzzification and predicted intensity processes to remove impulse noise. Sensitivity analysis of the proposed technique has been performed by varying the number of fuzzy sets. Experimental results demonstrate that the proposed technique achieves much better performance than state-of-the-art filters. The comparison of the results is based on global error measure as well as local error measures i.e. mean square error (MSE) and structural similarity index measure (SSIM).
Abstract: Mobile Ad hoc networks (MANET) are of dynamic and ever changing topologies which demand... more Abstract: Mobile Ad hoc networks (MANET) are of dynamic and ever changing topologies which demands, a new approach to design and develop an efficient routing strategy. There exists lot of routing protocols proposed so far in the literature. These protocols can broadly ...
Medical Principles and Practice, 2011