Yazar "Ashour, Amira S." seçeneğine göre listele

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    A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation
    (Elsevier, 2018) Ashour, Amira S.; Guo, Yanhui; Küçükkülahlı, Enver; Erdoğmuş, Pakize; Polat, Kemal
    In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm. (C) 2018 Elsevier B.V. All rights reserved.
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    A novel framework of two successive feature selection levels using weight-based procedure for voice-loss detection in parkinson's disease
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Ashour, Amira S.; Nour, Majid Kamal A.; Polat, Kemal; Guo, Yanhui; Alsaggaf, Wafaa; El-Attar, Amira
    Parkinson & x2019;s disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a novel feature selection framework using two levels of the feature selection procedure for voice-loss detection in PD patients. At the first level selection, the principal component analysis (PCA) and the eigenvector centrality feature selection (ECFS) methods are initially calculated independently, and the selected features from each method are considered as a separated sublist, namely ECFS selected features sublist, and PCA selected features sublist, in the first set. Accordingly, the first set, which is the first level selection set, is generated from the union of these two sublists using the top-selected features from both methods. In the training phase, a second level selection, which forms the second set (which is a subset from the first set), is generated to calculate the proposed weight of each selection method. Since in the present work, the ECFS provided superior performance to the PCA in the first level selection, the ECFS is applied to the first set in order to find weight values based on the contribution/impact of the top-selected PCA- and ECFS- features in the second level. This weight is determined by finding a proposed ratio, which is multiplied directly by the selected ECFS features in the first level. The selected weighted ECFS features are then combined with the same PCA features to avoid ignoring any of the top-ranked features from the first level. This combination includes the final weighted-hybrid selected features that fed to a support vector machine (SVM) classifier to evaluate the proposed weighted hybrid selected features. Hence, in the test phase, the generated weight is used directly without any further need for the second level selection. Several comparative studies were conducted to evaluate the proposed feature selection performance for PD voice-loss detection. The experimental results established the superiority of the proposed procedure using cubic kernel-SVM with 94 & x0025; accuracy for voice-loss detection in PD, while, with the same classifier, 88 & x0025; accuracy was achieved without using the proposed selection method.
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    OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation
    (Elsevier, 2020) Hawas, Ahmed Refaat; Guo, Yanhui; Du, Chunlai; Polat, Kemal; Ashour, Amira S.
    Automated skin lesion segmentation is one of the most crucial stages in dermoscopic images based diagnosis. To guarantee efficient unsupervised clustering-based segmentation, a histogram-based clustering estimation (HBCE) algorithm can be used to obtain the initial number of clusters with their corresponding centroids. Accordingly, the present work introduced a novel skin lesion segmentation algorithm, called optimized clustering estimation for neutrosophic graph cut algorithm (OCE-NGC). Firstly, the genetic algorithm (GA) is used to optimize the HBCE procedure by finding its optimal threshold values which are functions of a factor, called beta to be optimized. This optimization process guarantees the optimal determination of the initial number of clusters and their corresponding centroids for further use in the proposed clustering process. Thus, the skin lesion dermoscopic images are then mapped into the neutrosophic set (NS) domain which is computed by the neutrosophic c-means (NCM). The NCM groups the pixels in the dermoscopic images using the pre-determined optimal number of clusters obtained by the optimized HBCE. Finally, a cost function of the graph cut (GC) algorithm is defined in the NS domain for the segmentation process. The experimental results established the superiority of the proposed OCE-NGC approach in comparison with the traditional HBCE with NCM only, the traditional HBCE with the NGC, and the typical GC. In a public dataset, the proposed approach achieved 97.12% and 86.28% average accuracy and average Jaccard (JAC) values, respectively. (C) 2019 Elsevier B.V. All rights reserved.