Table of Content

Smart Solutions to Develop New Technologies for Healthcare

Submission Deadline: 25 August 2022 (closed)

Guest Editors

Dr. Youseef Alotaibi, Umm Al-Qura University, Saudi Arabia.
Dr. Osamah Ibrahim Khalaf, Al-Nahrain University, Iraq.


The world issues to deal with the pandemic caused by the pathogen SARS-CoV-2 has urgently posed the need of rethinking the available resources to combat a health crisis of this dimensions. Innovation in healthcare needs to be accelerated to address the health problems of our time and the future. Biomedical and healthcare data are available in different formats, including numeric, textual reports, images, and the data may come from different sources. A major challenge in biomedical science and healthcare involves coping with the uncertainty, imprecision and incompleteness. Such uncertainties make it difficult to develop useful models, algorithms, systems, and realizing their successful applications.

Although the current research in this field has shown promising results, there is an urgent need to explore novel data-driven knowledge discovery and analytics methods in clinical research to improve epidemic monitoring and healthcare delivery as a whole. Intelligent medicine and healthcare decision support systems have become an emerging research topic since they can be applied for disease diagnostics and/or prevention, follow-up monitoring, defining treatment pathways, clinical decision support etc

Despite the significant recent advances in medicine and healthcare data analysis, there are substantial research challenges and open questions to be explored. These demand further and deeper investigations to develop more useful decision-making systems that are capable of dealing with randomness, imprecision, volume, vagueness, incompleteness, and missing values along with efficient handling of variety, velocity and (abundant or lacking) volume of biomedical data. Compared to the traditional decision support techniques, the representation of fuzzy linguistic terms based on soft computing provides a straightforward framework for building more understandable, imprecision-aware clinical systems. As opposed to systems powered by statistical reasoning only, fuzzy biomedical systems cater a way of building models that encode the imprecise conceptual semantics of a health problem, not just for doing analytics, but also to embrace its interpretability. Thus, designing an efficient and effective fuzzy system to deal with uncertainty is an emerging and promising topic to improve reasoning and intelligent monitoring, control, diagnostic and treatment in biomedical science in healthcare.


Fuzzy systems for predicting and monitoring the spread of epidemic diseases
Fuzzy systems for measuring the damage of the epidemic disease
IT2 fuzzy sets for uncertain healthcare datasets
Fuzzy models for medical image classification/ diagnosis /recognition
Fuzzy data mining for brain-machine interfaces and medical signal analysis
Multi-objective evolutionary and adaptive fuzzy systems for handling epidemic disease
Fuzzy medicine and healthcare data mining based on the Hadoop or Spark platforms
Fuzzy approaches for neuroimaging and functional brain imaging processing of COVID-19

Published Papers

  • Open Access


    An Improved Fully Automated Breast Cancer Detection and Classification System

    Tawfeeq Shawly, Ahmed A. Alsheikhy
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433
    (This article belongs to this Special Issue: Smart Solutions to Develop New Technologies for Healthcare)
    Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier… More >

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