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Authors = Usama Pervaiz

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9 pages, 2672 KiB  
Benchmark
Fast PET Scan Tumor Segmentation Using Superpixels, Principal Component Analysis and K-Means Clustering
by Yeman Brhane Hagos, Vu Hoang Minh, Saed Khawaldeh, Usama Pervaiz and Tajwar Abrar Aleef
Methods Protoc. 2018, 1(1), 7; https://doi.org/10.3390/mps1010007 - 19 Jan 2018
Cited by 8 | Viewed by 4838
Abstract
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation has remained a challenging problem due [...] Read more.
Positron Emission Tomography scan images are extensively used in radiotherapy planning, clinical diagnosis, assessment of growth and treatment of a tumor. These all rely on fidelity and speed of detection and delineation algorithm. Despite intensive research, segmentation has remained a challenging problem due to the diverse image content, resolution, shape, and noise. This paper presents a fast positron emission tomography tumor segmentation method using superpixels. Principal component analysis is applied on the superpixels and their average value. The distance vector of each superpixel from the average is computed in the principal components coordinate system. Finally, k-means clustering is applied on the distance vector to recognize tumor and non-tumor superpixels. The proposed approach is implemented in MATLAB 2016A, and promising accuracy with execution time of 2.35 ± 0.26 s is achieved. Fast execution time is achieved since the number of superpixels, and the size of distance vector on which clustering was done are low compared to the number of pixels in the image. Full article
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17 pages, 1519 KiB  
Article
Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks
by Saed Khawaldeh, Usama Pervaiz, Azhar Rafiq and Rami S. Alkhawaldeh
Appl. Sci. 2018, 8(1), 27; https://doi.org/10.3390/app8010027 - 25 Dec 2017
Cited by 173 | Viewed by 8535
Abstract
In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for [...] Read more.
In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%. Full article
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11 pages, 4654 KiB  
Article
Taxonomic Classification for Living Organisms Using Convolutional Neural Networks
by Saed Khawaldeh, Usama Pervaiz, Mohammed Elsharnoby, Alaa Eddin Alchalabi and Nayel Al-Zubi
Genes 2017, 8(11), 326; https://doi.org/10.3390/genes8110326 - 17 Nov 2017
Cited by 13 | Viewed by 10574
Abstract
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living [...] Read more.
Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis. Full article
(This article belongs to the Section Technologies and Resources for Genetics)
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