Muhammad Irfan¹, Ahmad Shaf^2,*, Tariq Ali², Umar Farooq², Saifur Rahman¹, Salim Nasar Faraj Mursal¹, Mohammed Jalalah¹, Samar M. Alqhtani³, Omar AlShorman⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 711-729, 2023, DOI:10.32604/cmc.2023.038176

Abstract A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated… More >

Jieren Cheng^1,3, Xiulai Li^1,2,3,4,*, Xinbing Xu^2,3, Xiangyan Tang^1,3, Victor S. Sheng⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 975-992, 2023, DOI:10.32604/csse.2023.039280

Abstract With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important. However, due to its decentralized, decentralized nature, blockchain networks are vulnerable to distributed denial-of-service (DDoS) attacks, which can lead to service stops, causing serious economic losses and social impacts. The research questions in this paper mainly include two aspects: first, the classification of DDoS, which refers to detecting whether blockchain nodes are suffering DDoS attacks, that is, detecting the data of nodes in parallel; The second is the problem of DDoS segmentation, that is,… More >

Abeer D. Algarni^1,*, Walid El-Shafai², Ghada M. El Banby³, Fathi E. Abd El-Samie^1,2, Naglaa F. Soliman^1,4

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4393-4410, 2022, DOI:10.32604/cmc.2022.020265

Abstract COVID-19 remains to proliferate precipitously in the world. It has significantly influenced public health, the world economy, and the persons’ lives. Hence, there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients. With this explosion of this pandemic, there is a need for automated diagnosis tools to help specialists based on medical images. This paper presents a hybrid Convolutional Neural Network (CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography (CT) images. The proposed approach is employed to classify and segment the COVID-19, pneumonia, and normal CT images. The classification stage is… More >