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Search Results (21)
  • Open Access

    ARTICLE

    Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging

    Mohammad Mehedi Hassan1,*, Salman A. AlQahtani2, Mabrook S. AlRakhami1, Ahmed Zohier Elhendi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3101-3123, 2024, DOI:10.32604/cmes.2024.047940

    Abstract In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability… More >

  • Open Access

    ARTICLE

    Multilevel Attention Unet Segmentation Algorithm for Lung Cancer Based on CT Images

    Huan Wang1, Shi Qiu1,2,*, Benyue Zhang1, Lixuan Xiao3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1569-1589, 2024, DOI:10.32604/cmc.2023.046821

    Abstract Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements… More >

  • Open Access

    ARTICLE

    CD-FL: Cataract Images Based Disease Detection Using Federated Learning

    Arfat Ahmad Khan1, Shtwai Alsubai2, Chitapong Wechtaisong3,*, Ahmad Almadhor4, Natalia Kryvinska5,*, Abdullah Al Hejaili6, Uzma Ghulam Mohammad7

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1733-1750, 2023, DOI:10.32604/csse.2023.039296

    Abstract A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time. Automatic cataract prediction based on various imaging technologies has been addressed recently, such as smartphone apps used for remote health monitoring and eye treatment. In recent years, advances in diagnosis, prediction, and clinical decision support using Artificial Intelligence (AI) in medicine and ophthalmology have been exponential. Due to privacy concerns, a lack of data makes applying artificial intelligence models in the medical field challenging. To address this issue, a federated learning framework named CD-FLMore >

  • Open Access

    REVIEW

    A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection

    Shroog Alshomrani*, Muhammad Arif, Mohammed A. Al Ghamdi

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5717-5742, 2023, DOI:10.32604/cmc.2023.038059

    Abstract Coronavirus has infected more than 753 million people, ranging in severity from one person to another, where more than six million infected people died worldwide. Computer-aided diagnostic (CAD) with artificial intelligence (AI) showed outstanding performance in effectively diagnosing this virus in real-time. Computed tomography is a complementary diagnostic tool to clarify the damage of COVID-19 in the lungs even before symptoms appear in patients. This paper conducts a systematic literature review of deep learning methods for classifying the segmentation of COVID-19 infection in the lungs. We used the methodology of systematic reviews and meta-analyses (PRISMA) More >

  • Open Access

    ARTICLE

    A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

    Aghila Rajagopal1, Sultan Ahmad2,*, Sudan Jha3, Ramachandran Alagarsamy4, Abdullah Alharbi5, Bader Alouffi6

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3215-3229, 2023, DOI:10.32604/csse.2023.025705

    Abstract Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is… More >

  • Open Access

    ARTICLE

    Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

    Mahmoud Ragab1,2,3,*, Jaber Alyami4,5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877

    Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the… 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 More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

    Mohammad Alamgeer1, Hanan Abdullah Mengash2, Radwa Marzouk2, Mohamed K Nour3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4, Abu Sarwar Zamani4, Mohammed Rizwanullah4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1437-1448, 2022, DOI:10.32604/cmc.2022.027896

    Abstract Early detection of lung cancer can help for improving the survival rate of the patients. Biomedical imaging tools such as computed tomography (CT) image was utilized to the proper identification and positioning of lung cancer. The recently developed deep learning (DL) models can be employed for the effectual identification and classification of diseases. This article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image, named DLCADLC-BCT technique. The proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT images. The proposed DLCADLC-BCT technique initially uses gray level co-occurrence More >

  • Open Access

    ARTICLE

    Fast and Accurate Thoracic SPECT Image Reconstruction

    Afef Houimli1,*, IssamBen Mhamed2, Bechir Letaief1,3,4, Dorra Ben-Sellem1,3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 881-904, 2022, DOI:10.32604/cmes.2022.016705

    Abstract In Single-Photon Emission Computed Tomography (SPECT), the reconstructed image has insufficient contrast, poor resolution and inaccurate volume of the tumor size due to physical degradation factors. Generally, nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images. This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy. The suggested algorithm is composed of three steps. The first one consists of denoising… More >

  • Open Access

    ARTICLE

    Detection of Osteoarthritis Based on EHO Thresholding

    R. Kanthavel1,*, R. Dhaya2, Kanagaraj Venusamy3

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5783-5798, 2022, DOI:10.32604/cmc.2022.023745

    Abstract Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in More >

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