Mohamed Khaled Abu Mahmoud | University of Technology Sydney (original) (raw)

Papers by Mohamed Khaled Abu Mahmoud

ABSTRACT This paper proposes an automated non-invasive system for skin cancer (melanoma) detectio... more ABSTRACT This paper proposes an automated non-invasive system for skin cancer (melanoma) detection based on Support Vector Machine classification. The proposed system uses a number of features extracted from the Wavelet or the Curvelet decomposition of the grayscale skin lesion images and color features obtained from the original color images. The dataset used include both digital images and Dermoscopy images for skin lesions that are either benign or malignant. The recognition accuracy obtained by the Support Vector Machine classifier used in this experiment is 87.7.1% for the Wavelet based features and 83.6. 6% for the Curvelet based ones. The proposed system also resulted in a sensitivity of 86.4 % for the case of Wavelet and 76.9% for the case of Curvelet. It also resulted in a specificity of 88.1% for the case of Wavelet and 85.4% for the case of Curvelet. The obtained sensitivity and specificity results are comparable to those obtained by Dermatologists.

Humanoid Tumor is one of the utmost hazardous syndromes which is mostly affected by heritable unc... more Humanoid Tumor is one of the utmost hazardous syndromes which is mostly affected by heritable uncertainty of manifold molecular modifications. Midst numerous methods of humanoid tumor, Lung cancer is the utmost communal one. To classify Lung cancer at an initial phase and examine them over several procedures entitled as segmentation and feature extraction. Here, in this scheme is suggested to emphasis extraordinary attentiveness of Melanoma Heir which bases the Lung Cancer. This development is based on samples replica skill is used for malignant melanoma Lung tumor recognition. In this scheme dissimilar stage for melanoma Lung cancer lesion classification i.e., first the Image Gaining Method, preprocessing, separation, define piece for Lung cancer Feature Collection regulates lesion description, classification methods. In the Feature abstraction by numerical image treating method includes, regularity detection, Border Detection, color, and width discovery and also we used GLCM for excerpt the surface based features. Here we planned the Neural Network to categorize the benign or malignant stage.

Melanoma, the deadliest form of skin cancer, must be diagnosed early for effective treatment. Rou... more Melanoma, the deadliest form of skin cancer, must be diagnosed early for effective treatment. Rough pigment network and qualities are important signs for melanoma diagnosis using pathologist images. The main focus of this thesis is to improve skin cancer (Melanoma) detection through introducing novel image processing approach for a computer-aided system based on pigment network and elements detection on pathology images. It is important to propose an automated system for differentiating between

2014 World Symposium on Computer Applications & Research (WSCAR), 2014

ABSTRACT a novel methodology for automatic feature extraction from histo-pathological images and ... more ABSTRACT a novel methodology for automatic feature extraction from histo-pathological images and subsequent classification is presented. The proposed automated system use a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. The extracted features are reduced by using sequential feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve a good result with classification accuracy of (81)%, sensitivity of(76)% and specificity of (lOO)%and 17 times faster than some of the reported results.

ABSTRACT A gradient vector flow (GVF) snake is proposed in this paper for the segmentation of ski... more ABSTRACT A gradient vector flow (GVF) snake is proposed in this paper for the segmentation of skin cancer images. In order to make the snake insensitive to noise and be able to remove the hairs, an Adaptive Filter (Wiener and Median filters) is proposed. After the noise and hairs are removed, GVF snake will be used to segment the skin cancer region. The GVF snake extends the single direction and allows it to still be able to track the boundary of the skin cancer even if there are other objects near the skin cancer region. We have proposed new operators to find better edge map in a restored grey scale image. Subjective method has been used by comparing the performance of the proposed gradient vector flow (GVF) snake with other recommended operators of first derivative like Sobel, Prewitt, Roberts and second derivative like Laplacian. The root mean square error and root mean square of signal to noise ratio have been used for objective evaluation. Finally, to validate the efficiency of the filtering schemes different algorithms are proposed and the simulation study has been carried out. Experiments performed on 8(eight) cancer images show the effectiveness of the proposed algorithm.

2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is... more The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.

2011 11th International Conference on Hybrid Intelligent Systems (HIS), 2011

Proceedings of the 2014 International Symposium on Information Technology (ISIT 2014), Dalian, China, 14-16 October 2014, 2015

Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extrac... more Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histopathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT).This article introduces a novel melanoma detection strategy using a hybrid particle swarm-based support vector machine (SVM-WLG-PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.

International Journal of Electrical and Computer Engineering (IJECE)

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-patholo... more This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.

a novel methodology for automatic feature extraction from histo-pathological images and subsequen... more a novel methodology for automatic feature extraction from histo-pathological images and subsequent classification is presented. The proposed automated system use a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. The extracted features are reduced by using sequential feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve a good result with classification accuracy of (81)%, sensitivity of(76)% and specificity of (lOO)%

This paper proposes an automated system for discrimination between melanocytic nevi and malignant... more This paper proposes an automated system for discrimination between melanocytic nevi and malignant melanoma. The proposed system used a number of features extracted from histo-pathological images of skin lesions through image processing techniques which consisted of a spatially adaptive color median lter for ltering and a Kmeans clustering for segmentation. The extracted features were reduced by using sequential feature selection and then classied by using support vector machine (SVM) to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The proposed system was able to achieve a good result with classication accuracy of 88.9%, sensitivity of 87.5% and specicity of 100%.

this paper proposes an automatic skin cancer (melanoma) classification system. The input for the ... more this paper proposes an automatic skin cancer (melanoma) classification system. The input for the prosed system is a collected data images, it followed by different image processing procedures to enhance the image properties. Two segmentation methods used to identify the normal skin cancer from malignant skin and to extract the useful information from these images that passed to the classifier for training and testing. The features used for classification is the coefficients created by Wavelet decompositions and simple wrapper curvelet. Curvelet is suitable for the image that contains oriented texture and cartoon edges. Recognition accuracy of the three layers back-propagation neural network classifier with wavelet is 51.1% and with curvelet is 75. 6% in digital images database.

A gradient vector flow (GVF) snake is proposed in this paper for the segmentation of skin cancer ... more A gradient vector flow (GVF) snake is proposed in this paper for the segmentation of skin cancer images. In order to make the snake insensitive to noise and be able to remove the hairs, an Adaptive Filter (Wiener and Median filters) is proposed. After the noise and hairs are removed, GVF snake will be used to segment the skin cancer region. The GVF snake extends the single direction and allows it to still be able to track the boundary of the skin cancer even if there are other objects near the skin cancer region. We have proposed new operators to find better edge map in a restored grey scale image. Subjective method has been used by comparing the performance of the proposed gradient vector flow (GVF) snake with other recommended operators of first derivative like Sobel, Prewitt, Roberts and second derivative like Laplacian. The root mean square error and root mean square of signal to noise ratio have been used for objective evaluation. Finally, to validate the efficiency of the filtering schemes different algorithms are proposed and the simulation study has been carried out. Experiments performed on 8(eight) cancer images show the effectiveness of the proposed algorithm.

This paper proposes an automatic skin cancer (melanoma) classification system. The input for the ... more This paper proposes an automatic skin cancer (melanoma) classification system. The input for the proposed system is a set of images for benign and malignant skin lesions. Different image processing procedures such as smoothing and equalization are applied on these images to enhance their properties. Two segmentation methods are then used to identify the skin lesions before extracting the useful feature information from these images. This information is then passed to the classifier for training and testing. The features used for classification are coefficients created by Wavelet decompositions or simple wrapper Curvelets. Curvelets are known to be more suitable for the images that contain oriented textures and cartoon edges. The recognition accuracy obtained by the two layers back-propagation neural network classifier tested in this experiment is 58.44 % for the Wavelet based coefficients and 86.57 % for the Curvelet based ones.

2014 International Conference on Industrial Automation, Information and Communications Technology, 2014

Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extrac... more Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histopathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT).This article introduces a novel melanoma detection strategy using a hybrid particle swarm -based support vector machine (SVM-WLG -PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.

2014 World Symposium on Computer Applications & Research (WSCAR), 2014

2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013

2011 11th International Conference on Hybrid Intelligent Systems (HIS), 2011

Proceedings of the 2014 International Symposium on Information Technology (ISIT 2014), Dalian, China, 14-16 October 2014, 2015

International Journal of Electrical and Computer Engineering (IJECE)

2014 International Conference on Industrial Automation, Information and Communications Technology, 2014

Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extrac... more Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histopathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT).This article introduces a novel melanoma detection strategy using a hybrid particle swarm -based support vector machine (SVM-WLG -PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.