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  • Open Access


    AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

    S. Jayanthi*, S. Prasanna Devi

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2137-2158, 2024, DOI:10.32604/cmc.2024.045975

    Abstract In healthcare, the persistent challenge of arrhythmias, a leading cause of global mortality, has sparked extensive research into the automation of detection using machine learning (ML) algorithms. However, traditional ML and AutoML approaches have revealed their limitations, notably regarding feature generalization and automation efficiency. This glaring research gap has motivated the development of AutoRhythmAI, an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias. Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection, effectively bridging the gap between data preprocessing and model selection. To validate our system, we have rigorously… More >

  • Open Access


    Smart and Automated Diagnosis of COVID-19 Using Artificial Intelligence Techniques

    Masoud Alajmi1,*, Osama A. Elshakankiry2, Walid El-Shafai3, Hala S. El-Sayed4, Ahmed I. Sallam5, Heba M. El-Hoseny6, Ahmed Sedik7, Osama S. Faragallah2

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1403-1413, 2022, DOI:10.32604/iasc.2022.021211

    Abstract Machine Learning (ML) techniques have been combined with modern technologies across medical fields to detect and diagnose many diseases. Meanwhile, given the limited and unclear statistics on the Coronavirus Disease 2019 (COVID-19), the greatest challenge for all clinicians is to find effective and accurate methods for early diagnosis of the virus at a low cost. Medical imaging has found a role in this critical task utilizing a smart technology through different image modalities for COVID-19 cases, including X-ray imaging, Computed Tomography (CT) and magnetic resonance image (MRI) that can be used for diagnosis by radiologists. This paper combines ML with… More >

  • Open Access


    Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

    Vatsala Anand1, Sheifali Gupta1, Deepika Koundal2,*, Shubham Mahajan3, Amit Kant Pandit3, Atef Zaguia4

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3145-3160, 2022, DOI:10.32604/cmc.2022.022788

    Abstract Biomedical image analysis has been exploited considerably by recent technology involvements, carrying about a pattern shift towards ‘automation’ and ‘error free diagnosis’ classification methods with markedly improved accurate diagnosis productivity and cost effectiveness. This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images. The proposed model has four convolutional layers, two maxpool layers, one fully connected layer and three dense layers. All the convolutional layers are using the kernel size of 3 * 3 whereas the maxpool layer is using the kernel size of 2 * 2. The dermoscopy images… More >

  • Open Access


    Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

    Dezheng Zhang1,2, Qi Jia1,2, Shibing Yang1,2, Xinliang Han2, Cong Xu3, Xin Liu1,4, Yonghong Xie1,2,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 159-170, 2022, DOI:10.32604/cmc.2022.017295

    Abstract Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the different support strengths for the… More >

  • Open Access


    Deep Stacked Ensemble Learning Model for COVID-19 Classification

    G. Madhu1, B. Lalith Bharadwaj1, Rohit Boddeda2, Sai Vardhan1, K. Sandeep Kautish3, Khalid Alnowibet4, Adel F. Alrasheedi4, Ali Wagdy Mohamed5,6,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5467-5469, 2022, DOI:10.32604/cmc.2022.020455

    Abstract COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose… More >

  • Open Access


    An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

    Walid El-Shafai1, Noha A. El-Hag2, Ghada M. El-Banby3, Ashraf A. M. Khalaf2, Naglaa F. Soliman4,*, Abeer D. Algarni4, Fathi E. Abd El-Samie1,4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1323-1341, 2021, DOI:10.32604/cmc.2021.017385

    Abstract Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting segmented images are used to identify the infected region, whether COVID-19 or pneumonia. The proposed CNN consists of four Convolutional (Conv) layers, four batch normalization layers, and four Rectified… More >

  • Open Access


    A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images

    Mazin Abed Mohammed1, Karrar Hameed Abdulkareem2, Begonya Garcia-Zapirain3, Salama A. Mostafa4, Mashael S. Maashi5, Alaa S. Al-Waisy1, Mohammed Ahmed Subhi6, Ammar Awad Mutlag7, Dac-Nhuong Le8,9,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

    Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

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