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  • Open Access

    ARTICLE

    Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

    Saleh Albahli1,*, Ghulam Nabi Ahmad Hassan Yar2,3

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2743-2757, 2023, DOI:10.32604/csse.2023.024463

    Abstract Corona Virus (COVID-19) is a novel virus that crossed an animal-human barrier and emerged in Wuhan, China. Until now it has affected more than 119 million people. Detection of COVID-19 is a critical task and due to a large number of patients, a shortage of doctors has occurred for its detection. In this paper, a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas. Three classes have been defined; COVID-19, normal, and Pneumonia for X-ray images. For CT-Scan images, 2 classes have been defined COVID-19 and non-COVID-19. For… More >

  • Open Access

    ARTICLE

    A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

    Ali Alqahtani1, Shumaila Akram2, Muhammad Ramzan2,3,*, Fouzia Nawaz2, Hikmat Ullah Khan4, Essa Alhashlan5, Samar M. Alqhtani1, Areeba Waris6, Zain Ali7

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1721-1736, 2023, DOI:10.32604/iasc.2023.025597

    Abstract Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is to examine the X-radiation (X-rays)… More >

  • Open Access

    ARTICLE

    Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

    Nagwan Abdel Samee1, El-Sayed M. El-Kenawy2,3, Ghada Atteia1,*, Mona M. Jamjoom4, Abdelhameed Ibrahim5, Abdelaziz A. Abdelhamid6,7, Noha E. El-Attar8, Tarek Gaber9,10, Adam Slowik11, Mahmoud Y. Shams12

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4193-4210, 2022, DOI:10.32604/cmc.2022.031147

    Abstract As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures to control the spread of the pandemic. Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals, a rapid, reliable, and automatic detection of COVID-19 is in extreme need to curb the number of infections. By analyzing the COVID-19 chest X-ray images, a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers. The lung region was segmented from the original chest X-ray images and augmented using various… More >

  • Open Access

    ARTICLE

    Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

    Hussah Nasser AlEisa1, El-Sayed M. El-kenawy2,3, Amel Ali Alhussan1,*, Mohamed Saber4, Abdelaziz A. Abdelhamid5,6, Doaa Sami Khafaga1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447

    Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then… More >

  • Open Access

    ARTICLE

    Real-Time Multi-Class Infection Classification for Respiratory Diseases

    Ahmed ElShafee1, Walid El-Shafai2, Abdulaziz Alarifi3,*, Mohammed Amoon3, Aman Singh4, Moustafa H. Aly5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4157-4177, 2022, DOI:10.32604/cmc.2022.028847

    Abstract Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine. Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable, consistent, and timely, successfully lowering mortality rates, particularly during endemics and pandemics. To prevent this pandemic’s rapid and widespread, it is vital to quickly identify, confine, and treat affected individuals. The need for auxiliary computer-aided diagnostic (CAD) systems has grown. Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus. Utilizing advanced convolutional neural network (CNN) architectures in conjunction with radiological… More >

  • Open Access

    ARTICLE

    Hybrid Segmentation Approach for Different Medical Image Modalities

    Walid El-Shafai1,2, Amira A. Mahmoud1, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Naglaa F. Soliman3, Amel A. Alhussan4,*, Fathi E. Abd El-Samie1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3455-3472, 2022, DOI:10.32604/cmc.2022.028722

    Abstract The segmentation process requires separating the image region into sub-regions of similar properties. Each sub-region has a group of pixels having the same characteristics, such as texture or intensity. This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization (PSO) and improved fast fuzzy C-means clustering (IFFCM) algorithms. An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques. The existing medical image segmentation techniques incorporate clustering, thresholding, graph-based, edge-based, active contour, region-based, and watershed algorithms. This paper extensively analyzes and summarizes the… More >

  • Open Access

    ARTICLE

    A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray Image

    Sang-Hyun Lee*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4245-4260, 2022, DOI:10.32604/cmc.2022.026012

    Abstract X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by the lack of data to… More >

  • Open Access

    ARTICLE

    A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

    Yassir Edrees Almalki1, Maryam Zaffar2,*, Muhammad Irfan3, Mohammad Ali Abbas2, Maida Khalid2, K.S. Quraishi4, Tariq Ali5, Fahad Alshehri6, Sharifa Khalid Alduraibi6, Abdullah A. Asiri7, Mohammad Abd Alkhalik Basha8, Alaa Alduraibi6, M.K. Saeed7, Saifur Rahman3

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 163-180, 2023, DOI:10.32604/iasc.2023.025580

    Abstract The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients’ images of category X-rays are utilized to evaluate the performance of… More >

  • Open Access

    ARTICLE

    Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

    Joe Louis Paul Ignatius1,*, Sasirekha Selvakumar1, Kavin Gabriel Joe Louis Paul2, Aadhithya B. Kailash1, S. Keertivaas1, S. A. J. Akarvin Raja Prajan1

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 81-97, 2023, DOI:10.32604/csse.2023.025195

    Abstract Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This… More >

  • Open Access

    ARTICLE

    Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

    Jahangir Jabbar1, Muzammil Hussain2, Hassaan Malik2,*, Abdullah Gani3, Ali Haider Khan2, Muhammad Shiraz4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1827-1844, 2022, DOI:10.32604/cmc.2022.024965

    Abstract Wrist cracks are the most common sort of cracks with an excessive occurrence rate. For the routine detection of wrist cracks, conventional radiography (X-ray medical imaging) is used but periodically issues are presented by crack depiction. Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping. Indeed, many hospitals lack experienced clinicians to diagnose wrist cracks. Therefore, an automated system is required to reduce the burden on clinicians and identify cracks. In this study, we have designed a novel residual network-based convolutional neural network (CNN) for the crack detection of the wrist. For the… More >

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