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  • Open Access


    GUI-Based DL-Network Designer for KISTI’s Supercomputer Users

    Jaegwang Lee, Jongsuk R. Lee, Sunil Ahn*

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1611-1629, 2021, DOI:10.32604/cmc.2021.016803

    Abstract With the increase in research on AI (Artificial Intelligence), the importance of DL (Deep Learning) in various fields, such as materials, biotechnology, genomes, and new drugs, is increasing significantly, thereby increasing the number of deep-learning framework users. However, to design a deep neural network, a considerable understanding of the framework is required. To solve this problem, a GUI (Graphical User Interface)-based DNN (Deep Neural Network) design tool is being actively researched and developed. The GUI-based DNN design tool can design DNNs quickly and easily. However, the existing GUI-based DNN design tool has certain limitations such as poor usability, framework dependency,… More >

  • Open Access


    3D Semantic Deep Learning Networks for Leukemia Detection

    Javaria Amin1, Muhammad Sharif2, Muhammad Almas Anjum3, Ayesha Siddiqa1, Seifedine Kadry4, Yunyoung Nam5,*, Mudassar Raza2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 785-799, 2021, DOI:10.32604/cmc.2021.015249

    Abstract White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of… More >

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