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  • Open Access

    ARTICLE

    Optimized Predictive Framework for Healthcare Through Deep Learning

    Yasir Shahzad1,*, Huma Javed1, Haleem Farman2, Jamil Ahmad2, Bilal Jan3, Abdelmohsen A. Nassani4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2463-2480, 2021, DOI:10.32604/cmc.2021.014904

    Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling technique. The proposed model is… More >

  • Open Access

    ARTICLE

    Optimized Deep Learning-Inspired Model for the Diagnosis and Prediction of COVID-19

    Sally M. Elghamrawy1, Aboul Ella Hassnien2,*, Vaclav Snasel3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2353-2371, 2021, DOI:10.32604/cmc.2021.014767

    Abstract Detecting COVID-19 cases as early as possible became a critical issue that must be addressed to avoid the pandemic’s additional spread and early provide the appropriate treatment to the affected patients. This study aimed to develop a COVID-19 diagnosis and prediction (AIMDP) model that could identify patients with COVID-19 and distinguish it from other viral pneumonia signs detected in chest computed tomography (CT) scans. The proposed system uses convolutional neural networks (CNNs) as a deep learning technology to process hundreds of CT chest scan images and speeds up COVID-19 case prediction to facilitate its containment. We employed the whale optimization… More >

  • Open Access

    ARTICLE

    Recognition and Detection of Diabetic Retinopathy Using Densenet-65 Based Faster-RCNN

    Saleh Albahli1, Tahira Nazir2,*, Aun Irtaza2, Ali Javed3

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1333-1351, 2021, DOI:10.32604/cmc.2021.014691

    Abstract Diabetes is a metabolic disorder that results in a retinal complication called diabetic retinopathy (DR) which is one of the four main reasons for sightlessness all over the globe. DR usually has no clear symptoms before the onset, thus making disease identification a challenging task. The healthcare industry may face unfavorable consequences if the gap in identifying DR is not filled with effective automation. Thus, our objective is to develop an automatic and cost-effective method for classifying DR samples. In this work, we present a custom Faster-RCNN technique for the recognition and classification of DR lesions from retinal images. After… More >

  • Open Access

    ARTICLE

    Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

    Theyazn H. H. Aldhyani1,*, Melfi Alrasheed2, Mosleh Hmoud Al-Adaileh3, Ahmed Abdullah Alqarni4, Mohammed Y. Alzahrani4, Ahmed H. Alahmadi5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2141-2160, 2021, DOI:10.32604/cmc.2021.014498

    Abstract The Covid-19 epidemic poses a serious public health threat to the world, where people with little or no pre-existing human immunity can be more vulnerable to its effects. Thus, developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of lives. In this study, a deep learning algorithm and a Holt-trend model are proposed to predict the coronavirus. The Long-Short Term Memory (LSTM) and Holt-trend algorithms were applied to predict confirmed numbers and death cases. The real time data used has been collected from the World Health Organization (WHO). In the proposed research, we have… More >

  • Open Access

    ARTICLE

    Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

    Siddhant Banyal1, Rinky Dwivedi2, Koyel Datta Gupta2, Deepak Kumar Sharma3,*, Fadi Al-Turjman4, Leonardo Mostarda5

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1679-1696, 2021, DOI:10.32604/cmc.2021.014387

    Abstract The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine… More >

  • Open Access

    ARTICLE

    Exploiting Deep Learning Techniques for Colon Polyp Segmentation

    Daniel Sierra-Sosa1,*, Sebastian Patino-Barrientos2, Begonya Garcia-Zapirain3, Cristian Castillo-Olea3, Adel Elmaghraby1

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1629-1644, 2021, DOI:10.32604/cmc.2021.013618

    Abstract As colon cancer is among the top causes of death, there is a growing interest in developing improved techniques for the early detection of colon polyps. Given the close relation between colon polyps and colon cancer, their detection helps avoid cancer cases. The increment in the availability of colorectal screening tests and the number of colonoscopies have increased the burden on the medical personnel. In this article, the application of deep learning techniques for the detection and segmentation of colon polyps in colonoscopies is presented. Four techniques were implemented and evaluated: Mask-RCNN, PANet, Cascade R-CNN and Hybrid Task Cascade (HTC).… More >

  • Open Access

    ARTICLE

    COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

    A. S. Al-Waisy1, Mazin Abed Mohammed1, Shumoos Al-Fahdawi1, M. S. Maashi2, Begonya Garcia-Zapirain3, Karrar Hameed Abdulkareem4, S. A. Mostafa5, Nallapaneni Manoj Kumar6, Dac-Nhuong Le7,8,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2409-2429, 2021, DOI:10.32604/cmc.2021.012955

    Abstract Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited… More >

  • Open Access

    ARTICLE

    ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

    Yanyun Jiang1, Yuanjie Zheng1,2,*, Xiaodan Sui1, Wanzhen Jiao3, Yunlong He4, Weikuan Jia1

    Computer Systems Science and Engineering, Vol.36, No.3, pp. 537-549, 2021, DOI:10.32604/csse.2021.014578

    Abstract Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation… More >

  • Open Access

    ARTICLE

    Stock Price Forecasting: An Echo State Network Approach

    Guang Sun1, Jingjing Lin1,*, Chen Yang1, Xiangyang Yin1, Ziyu Li1, Peng Guo1,2, Junqi Sun3, Xiaoping Fan1, Bin Pan1

    Computer Systems Science and Engineering, Vol.36, No.3, pp. 509-520, 2021, DOI:10.32604/csse.2021.014189

    Abstract Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean… More >

  • Open Access

    ARTICLE

    An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

    Bin Yang1,*, Lingyun Xiang2, Xianyi Chen3, Wenjing Jia4

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 951-964, 2021, DOI:10.32604/cmc.2021.014839

    Abstract Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can predict diabetes more and more… More >

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