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  • Open Access

    ARTICLE

    A COVID-19 Detection Model Based on Convolutional Neural Network and Residual Learning

    Bo Wang1,*, Yongxin Zhang1, Shihui Ji2, Binbin Zhang1, Xiangyu Wang1, Jiyong Zhang1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3625-3642, 2023, DOI:10.32604/cmc.2023.036754 - 31 March 2023

    Abstract A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract… More >

  • Open Access

    ARTICLE

    Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

    José Escorcia-Gutierrez1,*, Roosvel Soto-Diaz2, Natasha Madera3, Carlos Soto3, Francisco Burgos-Florez2, Alexander Rodríguez4, Romany F. Mansour5

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1337-1353, 2023, DOI:10.32604/csse.2023.035253 - 09 February 2023

    Abstract Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep… More >

  • Open Access

    ARTICLE

    A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

    Hicham Moujahid1, Bouchaib Cherradi1,2,*, Oussama El Gannour1, Wamda Nagmeldin3, Abdelzahir Abdelmaboud4, Mohammed Al-Sarem5,6, Lhoussain Bahatti1, Faisal Saeed7, Mohammed Hadwan8,9

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1789-1809, 2023, DOI:10.32604/csse.2023.034022 - 09 February 2023

    Abstract Due to the rapid propagation characteristic of the Coronavirus (COVID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper,… More >

  • Open Access

    ARTICLE

    Deep Learning Model Ensemble for the Accuracy of Classification Degenerative Arthritis

    Sang-min Lee*, Namgi Kim

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1981-1994, 2023, DOI:10.32604/cmc.2023.035245 - 06 February 2023

    Abstract Artificial intelligence technologies are being studied to provide scientific evidence in the medical field and developed for use as diagnostic tools. This study focused on deep learning models to classify degenerative arthritis into Kellgren–Lawrence grades. Specifically, degenerative arthritis was assessed by X-ray radiographic images and classified into five classes. Subsequently, the use of various deep learning models was investigated for automating the degenerative arthritis classification process. Although research on the classification of osteoarthritis using deep learning has been conducted in previous studies, only local models have been used, and an ensemble of deep learning models… More >

  • Open Access

    ARTICLE

    Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

    Ha Manh Toan1, Lam Thanh Hien2, Ngo Duc Vinh3, Do Nang Toan1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5001-5016, 2023, DOI:10.32604/cmc.2023.033429 - 28 December 2022

    Abstract Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so… More >

  • Open Access

    ARTICLE

    Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

    Fuat Türk*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1357-1373, 2023, DOI:10.32604/csse.2023.030772 - 03 November 2022

    Abstract Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also… More >

  • Open Access

    ARTICLE

    Deep Attention Network for Pneumonia Detection Using Chest X-Ray Images

    Sukhendra Singh1, Sur Singh Rawat2, Manoj Gupta3, B. K. Tripathi4, Faisal Alanzi5, Arnab Majumdar6, Pattaraporn Khuwuthyakorn7, Orawit Thinnukool7,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1673-1691, 2023, DOI:10.32604/cmc.2023.032364 - 22 September 2022

    Abstract In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and… More >

  • Open Access

    ARTICLE

    CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

    Syeda Naila Batool, Ghulam Gilanie*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1319-1332, 2023, DOI:10.32604/cmc.2023.032121 - 22 September 2022

    Abstract Automated and autonomous decisions of image classification systems have essential applicability in this modern age even. Image-based decisions are commonly taken through explicit or auto-feature engineering of images. In forensic radiology, auto decisions based on images significantly affect the automation of various tasks. This study aims to assist forensic radiology in its biological profile estimation when only bones are left. A benchmarked dataset Radiology Society of North America (RSNA) has been used for research and experiments. Additionally, a locally developed dataset has also been used for research and experiments to cross-validate the results. A Convolutional… More >

  • Open Access

    ARTICLE

    A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

    Fatemeh Sadeghi1, Omid Rostami2, Myung-Kyu Yi3, Seong Oun Hwang3,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 751-768, 2023, DOI:10.32604/cmc.2023.031519 - 22 September 2022

    Abstract Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance… More >

  • Open Access

    ARTICLE

    Modeling of Crack Development Associated with Proppant Hydraulic Fracturing in a Clay-Carbonate Oil Deposit

    Sergey Galkin1,*, Ian Savitckii1, Denis Shustov1, Artyom Kukhtinskii1, Boris Osovetsky2, Alexander Votinov3

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.2, pp. 273-284, 2023, DOI:10.32604/fdmp.2022.021697 - 29 August 2022

    Abstract Survey and novel research data are used in the present study to classify/identify the lithological type of Verey age reservoirs’ rocks. It is shown how the use of X-ray tomography can clarify the degree of heterogeneity, porosity and permeability of these rocks. These data are then used to elaborate a model of hydraulic fracturing. The resulting software can take into account the properties of proppant and breakdown fluid, thermal reservoir conditions, oil properties, well design data and even the filtration and elastic-mechanical properties of the rocks. Calculations of hydraulic fracturing crack formation are carried out… More > Graphic Abstract

    Modeling of Crack Development Associated with Proppant Hydraulic Fracturing in a Clay-Carbonate Oil Deposit

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