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  • Open Access

    ARTICLE

    Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

    Ayman M. Abdalla1,*, Mohammad Abu Awad2, Omar AlZoubi2, La'aly A. Al-Samrraie3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 797-814, 2022, DOI:10.32604/cmc.2022.024913 - 24 February 2022

    Abstract The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine… More >

  • Open Access

    ARTICLE

    Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

    Mohamed Esmail Karar1,2, Marwa Ahmed Shouman3, Claire Chalopin4,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1683-1697, 2022, DOI:10.32604/cmc.2022.018564 - 07 September 2021

    Abstract The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with… More >

  • Open Access

    ARTICLE

    Multi-Level Fusion in Ultrasound for Cancer Detection Based on Uniform LBP Features

    Diyar Qader Zeebaree1, Adnan Mohsin Abdulazeez2, Dilovan Asaad Zebari3,*, Habibollah Haron4, Haza Nuzly Abdull Hamed4

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3363-3382, 2021, DOI:10.32604/cmc.2021.013314 - 28 December 2020

    Abstract Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains challenging. Despite the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise, an enhanced technique is not achieved. The purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern (LBP) and filtered noise reduction. To surmount the above limitations and achieve the aim of the study, a new descriptor that enhances the LBP features based on the new threshold has been proposed.… More >

  • Open Access

    ARTICLE

    Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

    Ihsan Jasim Hussein1, M. A. Burhanuddin2, Mazin Abed Mohammed3,*, Mohamed Elhoseny4, Begonya Garcia-Zapirain5, Marwah Suliman Maashi6, Mashael S. Maashi7

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3161-3182, 2021, DOI:10.32604/cmc.2021.012691 - 28 December 2020

    Abstract One of the most complex tasks for computer-aided diagnosis (Intelligent decision support system) is the segmentation of lesions. Thus, this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images. The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases. In addition, proposed an approach that can efficiently generate region-of-interest (ROI) and new features that can be used in characterizing lesion boundaries. This study uses two databases in training and testing the proposed… More >

  • Open Access

    ARTICLE

    Despeckling of Ultrasound Images Using Modified Local Statistics Mean Variance Filter

    Ranu Gupta1,3,*, Rahul Pachauri2,3, Ashutosh Singh1,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.1, pp. 19-32, 2018, DOI:10.3970/cmes.2018.114.019

    Abstract This article presents an improved method of despeckling the ultrasound medical images. In this paper a modified local statistics mean variance filter method has been proposed. In the proposed method, more consideration is given to local statistics since local statistical features are more important rather than global features.Various parameters like mean square error, peak signal to noise ratio, quality index, and structural similarity index measure are calculated to analyze the quality of the despeckled image. More >

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