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Intelligent Medical Decision Support Systems: Methods and Applications

Submission Deadline: 31 January 2025 View: 407 Submit to Special Issue

Guest Editors

Prof. Dr. Victor Hugo C. de Albuquerque, Federal University of Ceará, Brazil
Prof. Roberto Munoz, Universidad de Valparaíso, Chile
Prof. María de los Ángeles Quezada, Instituto Tecnológico de Tijuana, Mexico

Summary

In the last few decades, a significant progress has been made in the broad field of biomedical data analysis and processing, aiming at extracting relevant information directly from raw physiological data using cognitive systems. In particular, the automated analysis of these data has shown up as a promising strategy for assisting physicians in identifying hard-to-diagnosis pathologies, identifying a disease more quickly and, consequently, establishing a more appropriate and early treatment.

A great diversity of cognitive systems are applied in the biomedical engineering field, for instance, automation and control, signal/image processing and analysis, virtual and augmented reality, computer graphics, biomedical sensors, Internet of Health Things, among others. However, there are still many challenging problems involved in improving the accuracy, efficiency, and usability of these systems and problems related to designing, developing, and deploying new applications.

Thus, the main objective of this special issue is to bring together recent advances on new methods and applications of cognitive systems as support to medical diagnosis, grading and prognosis. We invite researchers to contribute original work related to this special issue, exploiting recent methodology using computational and mathematics techniques, proposing new ideas and directions for future development.

Potential topics include, but are not limited to:

· Data preprocessing, feature extraction, recognition, and matching for cognitive systems.

· Adaptive medical/signal processing and machine learning techniques for cognitive systems.

· Educational Technologies in Medical and Health Sciences Education

· Intelligent and multimodal cognitive systems.

· Automatic detection and diagnosis of diseases.

· Internet of Health Things.

· Cognitive Systems based on e-health and m-health technologies.

Keywords

Medical Image Processing; Signal Medical Processing; Medical Education

Published Papers

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ARTICLE

Transfer Learning Empowered Skin Diseases Detection in Children
Meena N. Alnuaimi, Nourah S. Alqahtani, Mohammed Gollapalli, Atta Rahman, Alaa Alahmadi, Aghiad Bakry, Mustafa Youldash, Dania Alkhulaifi, Rashad Ahmed, Hesham Al-Musallam
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2609-2623, 2024, DOI:10.32604/cmes.2024.055303
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Human beings are often affected by a wide range of skin diseases, which can be attributed to genetic factors and environmental influences, such as exposure to sunshine with ultraviolet (UV) rays. If left untreated, these diseases can have severe consequences and spread, especially among children. Early detection is crucial to prevent their spread and improve a patient’s chances of recovery. Dermatology, the branch of medicine dealing with skin diseases, faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance, type of skin, and… More >

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A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis
Hussain AlSalman, Taha Alfakih, Mabrook Al-Rakhami, Mohammad Mehedi Hassan, Amerah Alabrah
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

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ARTICLE

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images
Prasanalakshmi Balaji, Omar Alqahtani, Sangita Babu, Mousmi Ajay Chaurasia, Shanmugapriya Prakasam
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 443-458, 2024, DOI:10.32604/cmes.2024.053158
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Breast cancer is a significant threat to the global population, affecting not only women but also a threat to the entire population. With recent advancements in digital pathology, Eosin and hematoxylin images provide enhanced clarity in examining microscopic features of breast tissues based on their staining properties. Early cancer detection facilitates the quickening of the therapeutic process, thereby increasing survival rates. The analysis made by medical professionals, especially pathologists, is time-consuming and challenging, and there arises a need for automated breast cancer detection systems. The upcoming artificial intelligence platforms, especially deep learning models, play an More >

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ARTICLE

Explainable Artificial Intelligence (XAI) Model for Cancer Image Classification
Amit Singhal, Krishna Kant Agrawal, Angeles Quezada, Adrian Rodriguez Aguiñaga, Samantha Jiménez, Satya Prakash Yadav
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 401-441, 2024, DOI:10.32604/cmes.2024.051363
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability… More >

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ARTICLE

DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation
Chengzhang Zhu, Renmao Zhang, Yalong Xiao, Beiji Zou, Xian Chai, Zhangzheng Yang, Rong Hu, Xuanchu Duan
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine… More >

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ARTICLE

Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks
Haosong Gou, Gaoyi Zhang, Renê Ripardo Calixto, Senthil Kumar Jagatheesaperumal, Victor Hugo C. de Albuquerque
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1077-1102, 2024, DOI:10.32604/cmes.2024.047806
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve… More >

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ARTICLE

Enhancing Ulcerative Colitis Diagnosis: A Multi-Level Classification Approach with Deep Learning
Hasan J. Alyamani
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1129-1142, 2024, DOI:10.32604/cmes.2024.047756
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis, a condition with significant clinical implications. However, endoscopic assessment is susceptible to inherent variations, both within and between observers, compromising the reliability of individual evaluations. This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity. To initiate this endeavor, a multi-faceted approach is embarked upon. The dataset is meticulously preprocessed, enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization (CLAHE). A More >
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Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging
Mohammad Mehedi Hassan, Salman A. AlQahtani, Mabrook S. AlRakhami, Ahmed Zohier Elhendi
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3101-3123, 2024, DOI:10.32604/cmes.2024.047940
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
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… More >

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Intelligent Medical Decision Support Systems: Methods and Applications

Guest Editors

Summary

Keywords

Published Papers

View

428

Download

128

View

318

Download

127

View

404

Download

202

View

817

Download

316

View

748

Download

316

View

731

Download

392

View

650

Download

291

View

763

Download

365

Like

1

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