Special Issues

Deep Learning, IoT, and Blockchain in Medical Data Processing

Submission Deadline: 01 February 2024 (closed) View: 6

Guest Editors

Prof. Erfan Babaee Tirkolaee, Istinye University, Turkey
Prof. Ramin Ranjbarzadeh, Dublin City University, Ireland
Prof. Arunabha Mohan Roy, University of Michigan, USA


Deep Learning (DL), Internet of Things (IoT), and Blockchain technologies have emerged as transformative tools in various domains, including healthcare systems and medicine. DL tools, computational intelligence and optimization techniques have shown tremendous potential in medical data analysis, including image classification, diagnosis, and prediction. This proposal aims to explore the synergistic integration of these technologies in the context of medical data processing. By leveraging the capabilities of DL, IoT, and Blockchain, we can enhance the security, efficiency, and accuracy of medical data management and analysis, ultimately leading to improved patient care and outcomes.


Many attempts have been made so far that employ different techniques including, inter alia, Machine Learning (ML), neural networks, optimization, computational intelligence and human–machine interface. This special issue will mainly focus on the application of DL algorithms and optimization in processing and extracting valuable insights from medical data. We seek for topics such as DL-based image segmentation, disease diagnosis, and predictive modeling, showcasing the advancements made in these areas.


IoT has revolutionized the healthcare industry by enabling the connection of medical devices, wearables, and sensors to create a network of interconnected healthcare systems. This special issue will delve into the utilization of IoT in medical data processing. Topics of interest may include real-time patient monitoring, remote healthcare services, data collection from IoT devices, and the integration of IoT-generated data with DL models for improved diagnostics and personalized healthcare.


Blockchain technology offers a decentralized and immutable ledger that ensures transparency, security, and privacy in data management. In the context of medical data processing, Blockchain can provide secure and auditable storage of sensitive patient data, facilitate interoperability among healthcare systems, and enable patient-controlled data sharing. In this special issue, we will explore the applications of Blockchain in healthcare, including topics such as secure data sharing, consent management, clinical trials, and medical research.

The combination of DL, IoT, and Blockchain holds great potential for enhancing medical data processing. This special issue will also emphasize the integration of these technologies and explore innovative approaches and frameworks that leverage their complementary strengths. We encourage submissions that highlight successful use cases, novel methodologies, and interdisciplinary research bridging DL, IoT, and Blockchain in the context of medical data processing.

We invite researchers, practitioners, and industry experts to submit original research papers, case studies, and review articles related to the intersection of DL, IoT, and Blockchain in medical data processing. We encourage submissions that address challenges, propose novel methodologies, showcase practical applications, and explore the ethical considerations associated with these technologies. 


Potential topics include but are not limited to the following:

Computer-Assisted Diagnosis/Surgery

Data Mining and Big Data Analytics

Medical Image Processing

Natural Language Processing

Tracking and Preventing Diseases

Heart Failure Detection

Brain-Machine Interface

Artificial Neural Networks

DL Algorithms

Blockchain and IoT Applications

Automation by AI, ML and Neuroscience

Operations Research (OR)-based Analytics

Medical Decision-Making Techniques

Cancer Treatment Planning

Automation by Optimization and Control


Deep Learning, eHealth, Internet of Things, Bloinformatics, Computer vision

Published Papers

  • Open Access


    Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

    Nguyen Trong Vinh, Lam Thanh Hien, Ha Manh Toan, Ngo Duc Vinh, Do Nang Toan
    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 281-299, 2024, DOI:10.32604/iasc.2024.045297
    (This article belongs to the Special Issue: Deep Learning, IoT, and Blockchain in Medical Data Processing )
    Abstract Tuberculosis is a dangerous disease to human life, and we need a lot of attempts to stop and reverse it. Significantly, in the COVID-19 pandemic, access to medical services for tuberculosis has become very difficult. The late detection of tuberculosis could lead to danger to patient health, even death. Vietnam is one of the countries heavily affected by the COVID-19 pandemic, and many residential areas as well as hospitals have to be isolated for a long time. Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessing medical services,… More >

  • Open Access


    Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

    Do Nang Toan, Lam Thanh Hien, Ha Manh Toan, Nguyen Trong Vinh, Pham Trung Hieu
    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925
    (This article belongs to the Special Issue: Deep Learning, IoT, and Blockchain in Medical Data Processing )
    Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

  • Open Access


    Optimizing Deep Neural Networks for Face Recognition to Increase Training Speed and Improve Model Accuracy

    Mostafa Diba, Hossein Khosravi
    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590
    (This article belongs to the Special Issue: Deep Learning, IoT, and Blockchain in Medical Data Processing )
    Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories… More >

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