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  • Open Access


    Hybrid Single Image Super-Resolution Algorithm for Medical Images

    Walid El-Shafai1,2, Ehab Mahmoud Mohamed3,4,*, Medien Zeghid3,5, Anas M. Ali1,6, Moustafa H. Aly7

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4879-4896, 2022, DOI:10.32604/cmc.2022.028364

    Abstract High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to… More >

  • Open Access


    Efficient Forgery Detection Approaches for Digital Color Images

    Amira Baumy1, Abeer D. Algarni2,*, Mahmoud Abdalla3, Walid El-Shafai4,5, Fathi E. Abd El-Samie3,4, Naglaa F. Soliman2,3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3257-3276, 2022, DOI:10.32604/cmc.2022.021047

    Abstract This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position in the same image. The… More >

  • Open Access


    Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models

    Walid El-Shafai1, Anas M. Ali1,2, El-Sayed M. El-Rabaie1, Naglaa F. Soliman3,*, Abeer D. Algarni3, Fathi E. Abd El-Samie1,3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1141-1157, 2022, DOI:10.32604/cmc.2022.018547

    Abstract In developing countries, medical diagnosis is expensive and time consuming. Hence, automatic diagnosis can be a good cheap alternative. This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks (CNNs). These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists. The deep CNNs allow direct learning from the medical images. However, the accessibility of classified data is still the largest challenge, particularly in the field of medical imaging. Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs… More >

  • Open Access


    Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images

    G. R. Hemalakshmi*, D. Santhi, V. R. S. Mani, A. Geetha, N. B. Prakash

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 125-143, 2020, DOI:10.32604/cmes.2020.011331

    Abstract Diabetic retinopathy, aged macular degeneration, glaucoma etc. are widely prevalent ocular pathologies which are irreversible at advanced stages. Machine learning based automated detection of these pathologies facilitate timely clinical interventions, preventing adverse outcomes. Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images (FFA) which capture retinal components featuring diverse morphologies such as retinal vasculature, macula, optical disk etc. However, these images have low resolutions, hindering the accurate detection of ocular disorders. Construction of high resolution images from these images, by super resolution approaches expedites the diagnosis of pathologies with better accuracy. This paper presents a deep learning network for Single… More >

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