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  • Open Access

    ARTICLE

    Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

    Muhammad Armghan Latif1, Zohaib Mushtaq2, Saad Arif3, Sara Rehman4, Muhammad Farrukh Qureshi5, Nagwan Abdel Samee6, Maali Alabdulhafith6,*, Yeong Hyeon Gu7, Mohammed A. Al-masni7

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

    Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using… More >

  • Open Access

    ARTICLE

    Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

    M. Adimoolam1, K. Maithili2, N. M. Balamurugan3, R. Rajkumar4, S. Leelavathy5, Raju Kannadasan6, Mohd Anul Haq7,*, Ilyas Khan8, ElSayed M. Tag El Din9, Arfat Ahmad Khan10

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009

    Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using… More >

  • Open Access

    ARTICLE

    Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

    Haomiao Wang1, Jinxi Wang2, Qingmei Sui2,*, Faye Zhang2, Yibin Li1, Mingshun Jiang2, Phanasindh Paitekul3

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 91-110, 2024, DOI:10.32604/sdhm.2023.041522

    Abstract Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis. However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance. To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper. RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention. Combined with transfer learning techniques, the… More >

  • Open Access

    CASE REPORT

    Prenatal Diagnosis of an Apically Located Congenital Left Ventricular Aneurysm: A Rare Case

    Yücel Kaya1,*, And Yavuz1, Hasan Berkan Sayal1, Büşra Tsakir1, Gökalp Kabacaoğlu1, Kadriye Nilay Özcan2

    Congenital Heart Disease, Vol.19, No.1, pp. 123-129, 2024, DOI:10.32604/chd.2024.048145

    Abstract Congenital ventricular aneurysm is a very rare cardiac anomaly. A diagnosis can be made during the prenatal period using fetal echocardiography. This study presents a very rare apically located left ventricular aneurysm case, and the relevant literature was reviewed and discussed. In this case, a 35-year-old, gravida 2, parity 1 pregnant woman at 24 weeks of gestation, displayed a wide aneurysmal image in the left ventricular apical wall on fetal echocardiography. There was a 1.79 mm muscular ventricular septal defect at the apical region of the interventricular septum. In the course of the color Doppler ultrasonography examination, an aberrant fibrous… More >

  • 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 of standard Convolutional Neural Network… More >

  • Open Access

    ARTICLE

    Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

    Jinbo Yang1, Hai Huang1, Lailai Yin2, Jiaxing Qu3, Wanjuan Xie4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3085-3099, 2024, DOI:10.32604/cmes.2023.045417

    Abstract Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients’ data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should… 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 can be encapsulated as follows:… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    An Assisted Diagnosis of Alzheimer’s Disease Incorporating Attention Mechanisms Med-3D Transfer Modeling

    Yanmei Li1,*, Jinghong Tang1, Weiwu Ding1, Jian Luo2, Naveed Ahmad3, Rajesh Kumar4

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2023.046872

    Abstract Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder. The subtle and insidious onset of its pathogenesis makes early detection of a formidable challenge in both contemporary neuroscience and clinical practice. In this study, we introduce an advanced diagnostic methodology rooted in the Med-3D transfer model and enhanced with an attention mechanism. We aim to improve the precision of AD diagnosis and facilitate its early identification. Initially, we employ a spatial normalization technique to address challenges like clarity degradation and unsaturation, which are commonly observed in imaging datasets. Subsequently, an attention mechanism is incorporated to selectively focus on the salient… More >

  • Open Access

    ARTICLE

    Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

    Kwok Tai Chui1,*, Brij B. Gupta2,3,4,5,6,*, Varsha Arya7,8,9, Miguel Torres-Ruiz10

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762

    Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >

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