Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (3)
  • Open Access

    ARTICLE

    Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

    Fadl Dahan*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921 - 21 May 2024

    Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >

  • Open Access

    ARTICLE

    An Algorithm to Reduce Compression Ratio in Multimedia Applications

    Dur-e-Jabeen1,*, Tahmina Khan2, Rumaisa Iftikhar1, Ali Akbar Siddique1, Samiya Asghar1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 539-557, 2023, DOI:10.32604/cmc.2023.032393 - 22 September 2022

    Abstract In recent years, it has been evident that internet is the most effective means of transmitting information in the form of documents, photographs, or videos around the world. The purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual quality. During transmission, this massive data necessitates a lot of channel space. In order to overcome this problem, an effective visual compression approach is required to resize this large amount of data. This work is based on lossy image compression and is offered for static color… More >

  • Open Access

    ARTICLE

    Text Detection and Classification from Low Quality Natural Images

    Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775 - 24 December 2020

    Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image.… More >

Displaying 1-10 on page 1 of 3. Per Page