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  • Open Access


    Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

    Abdullah A. Asiri1, Ahmad Shaf2,*, Tariq Ali2, Muhammad Aamir2, Ali Usman2, Muhammad Irfan3, Hassan A. Alshamrani1, Khlood M. Mehdar4, Osama M. Alshehri5, Samar M. Alqhtani6

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 127-143, 2023, DOI:10.32604/iasc.2023.032391

    Abstract The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique in which two neural networks… More >

  • Open Access


    Using GAN Neural Networks for Super-Resolution Reconstruction of Temperature Fields

    Tao Li1, Zhiwei Jiang1,*, Rui Han2, Jinyue Xia3, Yongjun Ren4

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 941-956, 2023, DOI:10.32604/iasc.2023.029644

    Abstract A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead of transposed convolution or interpolation… More >

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