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  • Open Access


    Image Splicing Detection Using Generalized Whittaker Function Descriptor

    Dumitru Baleanu1,2,3, Ahmad Sami Al-Shamayleh4, Rabha W. Ibrahim5,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3465-3477, 2023, DOI:10.32604/cmc.2023.037162

    Abstract Image forgery is a crucial part of the transmission of misinformation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research.… More >

  • Open Access


    Hybrid Color Texture Features Classification Through ANN for Melanoma

    Saleem Mustafa1, Arfan Jaffar1, Muhammad Waseem Iqbal2,*, Asma Abubakar2, Abdullah S. Alshahrani3, Ahmed Alghamdi4

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549

    Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

  • Open Access


    Time-Efficient Fire Detection Convolutional Neural Network Coupled with Transfer Learning

    Hanan A. Hosni Mahmoud, Amal H. Alharbi, Norah S. Alghamdi*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1393-1403, 2022, DOI:10.32604/iasc.2022.020629

    Abstract The detection of fires in surveillance videos are usually done by utilizing deep learning. In Spite of the advances in processing power, deep learning methods usually need extensive computations and require high memory resources. This leads to restriction in real time fire detection. In this research, we present a time-efficient fire detection convolutional neural network coupled with transfer learning for surveillance systems. The model utilizes CNN architecture with reasonable computational time that is deemed possible for real time applications. At the same time, the model will not compromise accuracy for time efficiency by tuning the model with respect to fire… More >

  • Open Access


    Real Time Feature Extraction Deep-CNN for Mask Detection

    Hanan A. Hosni Mahmoud, Norah S. Alghamdi, Amal H. Alharbi*

    Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1423-1434, 2022, DOI:10.32604/iasc.2022.020586

    Abstract COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique… More >

  • Open Access


    Image Splicing Detection Based on Texture Features with Fractal Entropy

    Razi J. Al-Azawi1, Nadia M. G. Al-Saidi2, Hamid A. Jalab3,*, Rabha W. Ibrahim4, Dumitru Baleanu5,6,7

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3903-3915, 2021, DOI:10.32604/cmc.2021.020368

    Abstract Over the past years, image manipulation tools have become widely accessible and easier to use, which made the issue of image tampering far more severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is… More >

  • Open Access


    Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

    Hanan A. Hosni Mahmoud*, Amal H. Alharbi, Doaa S. Khafga

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 803-814, 2021, DOI:10.32604/iasc.2021.018607

    Abstract In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate… More >

  • Open Access


    Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

    Tina Babu1, Deepa Gupta1, Tripty Singh1,*, Shahin Hameed2, Mohammed Zakariah3, Yousef Ajami Alotaibi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 99-128, 2021, DOI:10.32604/cmc.2021.016341

    Abstract Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized… More >

  • Open Access


    DeepFake Videos Detection Based on Texture Features

    Bozhi Xu1, Jiarui Liu1, Jifan Liang1, Wei Lu1,*, Yue Zhang2

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1375-1388, 2021, DOI:10.32604/cmc.2021.016760

    Abstract In recent years, with the rapid development of deep learning technologies, some neural network models have been applied to generate fake media. DeepFakes, a deep learning based forgery technology, can tamper with the face easily and generate fake videos that are difficult to be distinguished by human eyes. The spread of face manipulation videos is very easy to bring fake information. Therefore, it is important to develop effective detection methods to verify the authenticity of the videos. Due to that it is still challenging for current forgery technologies to generate all facial details and the blending operations are used in… More >

  • Open Access


    Intravascular Optical Coherence Tomography Image Segmentation Based on Support Vector Machine Algorithm

    Yuxiang Huang1, Chuliu He1, Jiaqiu Wang2, Yuehong Miao1, Tongjin Zhu1, Ping Zhou1, Zhiyong Li1,2,*

    Molecular & Cellular Biomechanics, Vol.15, No.2, pp. 117-125, 2018, DOI: 10.3970/mcb.2018.02478

    Abstract Intravascular optical coherence tomography (IVOCT) is becoming more and more popular in clinical diagnosis of coronary atherosclerotic. However, reading IVOCT images is of large amount of work. This article describes a method based on image feature extraction and support vector machine (SVM) to achieve semi-automatic segmentation of IVOCT images. The image features utilized in this work including light attenuation coefficients and image textures based on gray level co-occurrence matrix. Different sets of hyper-parameters and image features were tested. This method achieved an accuracy of 83% on the test images. Single class accuracy of 89% for fibrous, 79.3% for calcification and… More >

  • Open Access


    The Correlation Between Texture Features and Fibrous Cap Thickness of Lipid-Rich Atheroma Based on Optical Coherence Tomography Imaging

    Chunliu He1, Jiaqiu Wang2, Yuxiang Huang1, Tongjing Zhu1, Yuehong Miao1, Zhiyong Li1,2*

    Molecular & Cellular Biomechanics, Vol.13, No.1, pp. 23-36, 2016, DOI:10.3970/mcb.2016.013.027

    Abstract Fibrous cap thickness (FCT) is seen as critical to plaque vulnerability. Therefore, the development of automatic algorithms for the quantification of FCT is for estimating cardiovascular risk of patients. Intravascular optical coherence tomography (IVOCT) is currently the only in vivo imaging modality with which FCT, the critical component of plaque vulnerability, can be assessed accurately. This study was aimed to discussion the correlation between the texture features of OCT images and the FCT in lipid-rich atheroma. Methods: Firstly, a full automatic segmentation algorithm based on unsupervised fuzzy c means (FCM) clustering with geometric constrains was developed to segment the ROIs… More >

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