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  • Open Access


    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… More >

  • Open Access


    Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis

    Ahmad Alassaf*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2773-2789, 2023, DOI:10.32604/csse.2023.035899

    Abstract Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare. Deep Learning (DL) models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models. On the other hand, skin lesion-based segregation and disintegration procedures play an essential role in earlier skin cancer detection. However, artefacts, an unclear boundary, poor contrast, and different lesion sizes make detection difficult. To address the issues in skin lesion diagnosis, this study creates the UDLS-DDOA model, an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder (UDLS) optimized by Dynamic Differential… More >

  • Open Access


    An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

    Ruichao Zhang1,*, Liqiang Wang1, Dechun Chen2

    Energy Engineering, Vol.118, No.4, pp. 1069-1082, 2021, DOI:10.32604/EE.2021.014961

    Abstract In recent years, deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields. In the diagnosis of sucker-rod pump working conditions, due to the lack of a large-scale dynamometer card data set, the advantages of a deep convolutional neural network are not well reflected, and its application is limited. Therefore, this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning, which is used to solve the problem of too few samples in a… More >

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