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  • Open Access

    ARTICLE

    Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

    Jing Lu1, Yan Wu2,#, Mingyan Hu1, Yao Xiong1, Yapeng Zhou1, Ziliang Zhao1, Liutong Shang1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897

    Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based on VGG16, Inception… More >

  • Open Access

    ARTICLE

    BEVGGC: Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images

    Junding Sun1,3,#, Xiang Li1,#, Chaosheng Tang1,*, Shixin Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 729-753, 2021, DOI:10.32604/cmes.2021.016416

    Abstract Purpose: As to January 11, 2021, coronavirus disease (COVID-19) has caused more than 2 million deaths worldwide. Mainly diagnostic methods of COVID-19 are: (i) nucleic acid testing. This method requires high requirements on the sample testing environment. When collecting samples, staff are in a susceptible environment, which increases the risk of infection. (ii) chest computed tomography. The cost of it is high and some radiation in the scan process. (iii) chest X-ray images. It has the advantages of fast imaging, higher spatial recognition than chest computed tomography. Therefore, our team chose the chest X-ray images as the experimental dataset in… More >

  • Open Access

    ARTICLE

    VISPNN: VGG-Inspired Stochastic Pooling Neural Network

    Shui-Hua Wang1, Muhammad Attique Khan2, Yu-Dong Zhang3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3081-3097, 2022, DOI:10.32604/cmc.2022.019447

    Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image).… More >

  • Open Access

    ARTICLE

    Defect Detection in Printed Circuit Boards with Pre-Trained Feature Extraction Methodology with Convolution Neural Networks

    Mohammed A. Alghassab*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 637-652, 2022, DOI:10.32604/cmc.2022.019527

    Abstract Printed Circuit Boards (PCBs) are very important for proper functioning of any electronic device. PCBs are installed in almost all the electronic device and their functionality is dependent on the perfection of PCBs. If PCBs do not function properly then the whole electric machine might fail. So, keeping this in mind researchers are working in this field to develop error free PCBs. Initially these PCBs were examined by the human beings manually, but the human error did not give good results as sometime defected PCBs were categorized as non-defective. So, researchers and experts transformed this manual traditional examination to automated… More >

  • Open Access

    ARTICLE

    Deep Optimal VGG16 Based COVID-19 Diagnosis Model

    M. Buvana1, K. Muthumayil2, S. Senthil kumar3, Jamel Nebhen4, Sultan S. Alshamrani5, Ihsan Ali6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 43-58, 2022, DOI:10.32604/cmc.2022.019331

    Abstract Coronavirus (COVID-19) outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up Robust Features (SURF), Features from… More >

  • Open Access

    ARTICLE

    Screening of COVID-19 Patients Using Deep Learning and IoT Framework

    Harshit Kaushik1, Dilbag Singh2, Shailendra Tiwari3, Manjit Kaur2, Chang-Won Jeong4, Yunyoung Nam5,*, Muhammad Attique Khan6

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3459-3475, 2021, DOI:10.32604/cmc.2021.017337

    Abstract In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for… More >

  • Open Access

    ARTICLE

    Generating Cartoon Images from Face Photos with Cycle-Consistent Adversarial Networks

    Tao Zhang1,2, Zhanjie Zhang1,2,*, Wenjing Jia3, Xiangjian He3, Jie Yang4

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2733-2747, 2021, DOI:10.32604/cmc.2021.019305

    Abstract The generative adversarial network (GAN) is first proposed in 2014, and this kind of network model is machine learning systems that can learn to measure a given distribution of data, one of the most important applications is style transfer. Style transfer is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image. CYCLE-GAN is a classic GAN model, which has a wide range of scenarios in style transfer. Considering its unsupervised learning characteristics, the mapping is easy to be learned between an input image and an output… More >

  • Open Access

    ARTICLE

    COVID-19 Diagnosis Using Transfer-Learning Techniques

    Mohammed Faisal1,*, Fahad Albogamy2, Hebah ElGibreen3, Mohammed Algabri3, Syed Ahad M. Alvi1, Mansour Alsulaiman3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 649-667, 2021, DOI:10.32604/iasc.2021.017898

    Abstract COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis… More >

  • Open Access

    ARTICLE

    DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification

    Sara Saber1, Uswah Khairuddin2,*, Rubiyah Yusof2, Ahmed Madani1

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3563-3576, 2021, DOI:10.32604/cmc.2021.017769

    Abstract The identification of DNA binding proteins (DNABPs) is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g., in the study of the biological, biophysical, and biochemical effects of antibiotics, drugs, and steroids on DNA. This paper presents an efficient approach for DNABPs identification based on deep transfer learning, named “DTLM-DBP.” Two transfer learning methods are used in the identification process. The first is based on the pre-trained deep learning model as a feature’s extractor and classifier. Two different pre-trained Convolutional Neural Networks (CNN), AlexNet 8 and VGG… More >

  • Open Access

    ARTICLE

    Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

    Kathiravan Srinivasan1, Lalit Garg2,*, Debajit Datta3, Abdulellah A. Alaboudi4, N. Z. Jhanjhi5, Rishav Agarwal3, Anmol George Thomas1

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4109-4124, 2021, DOI:10.32604/cmc.2021.016736

    Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research… More >

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