Special lssues

Artificial Intelligence based Healthcare Systems

Submission Deadline: 30 March 2023 (closed)

Guest Editors

Dr. Mujeeb Ur Rehman, Riphah International University, Pakistan.
Dr. Rab Nawaz, University of Glasgow, UK.
Dr. Rehmat Ullah, Queen's University Belfast, UK.


The early diagnosis of a disease is vital in medical science and would be a prerequisite for prophylactic treatments. A timely diagnosis followed by treatment and precaution can save human lives. Testing the feasibility of disease biomarkers and providing novel artificial intelligence-based treatments are essential. In this regard, Computer-Aided Diagnosis (CAD), also called Complementary Medicine Technique (CMT), supported by Artificial Intelligence (AI), appears to be reliable, indispensable, robust, and accurate. CAD/CMT is gaining significant popularity as a diagnostic tool, especially for diseases where the mainstream diagnosis is painful and less precise. Further, proliferated utilization of CAD/CMT has been observed because of its reliability and accuracy. AI has gained popularity for solving numerous real-world problems in the last few years. With the help of machine learning (ML) and Deep Learning (DL), AI can be used for diagnosis, prognosis, monitoring, and administration of treatment to enhance patients' health outcomes. Furthermore, AI has aided medical practitioners in lowering diagnostic errors and increasing precision. In exchange, it has saved the human body's most critical organs and reduced burdens on hospitals. Early detection of diseases such as breast cancer, diabetic's disease, Liver and lungs diseases, viral diseases, Alzheimer's disease, and cardiovascular disorders has been made possible through AI. Due to accuracy and reliability, many researchers have focused on illness diagnosis using AI-based diagnostic techniques. We invite academics to submit original research articles and review articles that examine novel AI, machine learning, and deep learning-based medical diagnosis and prevention systems.


Artificial Intelligence; Computer-Aided Diagnosis; Complementary Medicine Technique; Machine Learning; Deep Learning; Signal processing; Biomedical signal analysis

Published Papers

  • Open Access


    Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

    Jayashree Shetty, Nisha P. Shetty, Hrushikesh Kothikar, Saleh Mowla, Aiswarya Anand, Veeraj Hegde
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206
    (This article belongs to this Special Issue: Artificial Intelligence based Healthcare Systems)
    Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different… More >

  • Open Access


    Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

    Amani Yahyaoui, Jawad Rasheed, Shtwai Alsubai, Raed M. Shubair, Abdullah Alqahtani, Buket Isler, Rana Zeeshan Haider
    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2247-2261, 2023, DOI:10.32604/iasc.2023.036840
    (This article belongs to this Special Issue: Artificial Intelligence based Healthcare Systems)
    Abstract The coronavirus (COVID-19) is a disease declared a global pandemic that threatens the whole world. Since then, research has accelerated and varied to find practical solutions for the early detection and correct identification of this disease. Several researchers have focused on using the potential of Artificial Intelligence (AI) techniques in disease diagnosis to diagnose and detect the coronavirus. This paper developed deep learning (DL) and machine learning (ML) -based models using laboratory findings to diagnose COVID-19. Six different methods are used in this study: K-nearest neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) as a machine learning method, and… More >

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