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  • Open Access

    ARTICLE

    Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

    Sobia Nawaz1, Sidra Rasheed2, Wania Sami3, Lal Hussain4,5,*, Amjad Aldweesh6,*, Elsayed Tag eldin7, Umair Ahmad Salaria8,9, Mohammad Shahbaz Khan10

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5213-5228, 2023, DOI:10.32604/cmc.2023.037543

    Abstract This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The… More >

  • Open Access

    ARTICLE

    Automated X-ray Defect Inspection on Occluded BGA Balls Using Hybrid Algorithm

    Ki-Yeol Eom1, Byungseok Min2,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6337-6350, 2023, DOI:10.32604/cmc.2023.035336

    Abstract Automated X-ray defect inspection of occluded objects has been an essential topic in semiconductors, autonomous vehicles, and artificial intelligence devices. However, there are few solutions to segment occluded objects in the X-ray inspection efficiently. In particular, in the Ball Grid Array inspection of X-ray images, it is difficult to accurately segment the regions of occluded solder balls and detect defects inside solder balls. In this paper, we present a novel automatic inspection algorithm that segments solder balls, and detects defects fast and efficiently when solder balls are occluded. The proposed algorithm consists of two stages. In the first stage, the… More >

  • Open Access

    ARTICLE

    Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

    José Escorcia-Gutierrez1,*, Margarita Gamarra1, Roosvel Soto-Diaz2, Safa Alsafari3, Ayman Yafoz4, Romany F. Mansour5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5255-5270, 2023, DOI:10.32604/cmc.2023.033731

    Abstract A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This… More >

  • Open Access

    ARTICLE

    COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

    Ali Akbar Siddique1, S. M. Umar Talha1, M. Aamir1, Abeer D. Algarni2, Naglaa F. Soliman2,*, Walid El-Shafai3,4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3883-3901, 2023, DOI:10.32604/cmc.2023.037413

    Abstract The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and… More >

  • 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

    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 the COVID-19 feature information by… 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

    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 Transfer Learning Enabled Tuberculosis Classification… 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

    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, we propose a novel Convolutional… 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

    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 has never been applied to… 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

    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 early and simple detection… More >

  • Open Access

    ARTICLE

    X-ray Based COVID-19 Classification Using Lightweight EfficientNet

    Tahani Maazi Almutairi*, Mohamed Maher Ben Ismail, Ouiem Bchir

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 167-187, 2022, DOI:10.32604/jai.2022.032974

    Abstract The world has been suffering from the Coronavirus (COVID-19) pandemic since its appearance in late 2019. COVID-19 spread has led to a drastic increase of the number of infected people and deaths worldwide. Imminent and accurate diagnosis of positive cases emerged as a natural alternative to reduce the number of serious infections and limit the spread of the disease. In this paper, we proposed an X-ray based COVID-19 classification system that aims at diagnosing positive COVID-19 cases. Specifically, we adapted lightweight versions of EfficientNet as backbone of the proposed recognition system. Particularly, lightweight EfficientNet networks were used to build classification… More >

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