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Telehealth Monitoring with Man-Computer Interface for Medical Processing

Submission Deadline: 01 August 2023 (closed)

Guest Editors

Dr. Muhammad Attique Khan, HITEC University Taxila, Pakistan.
Dr. Shuihua Wang, University of Leicester, UK.
Dr. Syed Ahmad Chan Bukhari, St. John's University, New York, USA.

Summary

Recently, the computerized scheme supported disease diagnosis is common in hospitals. These methods are employed for; disease screening, pre/post-processing, knowledge extraction from the data, disease prediction, treatment planning, execution, and recovery monitoring. The advanced disease diagnosis involves employing advanced procedures, like the body-area network to collect the information, multi-sensor and multi-data collection during the screening, data assessment using modern computer algorithms, and Artificial Intelligence (AI) schemes for efficient data sharing, storage, and retrieval with modern methods.
In the current era, advanced data handling methods, like big data processing, medical cloud-supported data handling, and blockchain, are commonly adopted in modern hospitals to support effective data processing and disease handling. Further, the recently developed man-computer interface (MCI) schemes will support virtual-reality approaches, telehealth monitoring and patient care, internet of things-based data handling and treatment, and precision medicine to ensure the appropriate treatment for the patient. Integrating these modern schemes will create an efficient environment in which the patient-computer and computer-doctor integration is effectively utilized to implement real-time patient monitoring from a remote location.
This special issue focuses on collecting the current research works related to medical data assessment with the man-computer interface from the scientists and researchers. This special issue also welcomes real clinical works and review works from the doctors.


Keywords

• Skin cancer diagnosis using big data
• Block chain technology for skin cancer diagnosis
• Medical cloud support system for skin cancer in dermoscopic images
• Brain tumor classification using big data and medical cloud
• Breast cancer diagnosis based on Big data and block chain
• Gastrointestinal diseases 
• Covid19 classification using Big Data
• IoT and block chain technology for medical cancer diagnosis
• MCI based breast cancer diagnosis and recognition
• Skin cancer diagnosis using MCI
• MCI based radiologists cancer diagnosis
• MCI based brain tumor diagnosis and classification

Published Papers


  • Open Access

    ARTICLE

    A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

    Maryam Bukhari, Sadaf Yasmin, Sheneela Naz, Mehr Yahya Durrani, Mubashir Javaid, Jihoon Moon, Seungmin Rho
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1251-1279, 2023, DOI:10.32604/cmc.2023.040329
    (This article belongs to this Special Issue: Telehealth Monitoring with Man-Computer Interface for Medical Processing)
    Abstract Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and… More >

  • Open Access

    ARTICLE

    SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

    Jiaji Wang, Muhammad Attique Khan, Shuihua Wang, Yudong Zhang
    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2201-2216, 2023, DOI:10.32604/cmc.2023.041191
    (This article belongs to this Special Issue: Telehealth Monitoring with Man-Computer Interface for Medical Processing)
    Abstract Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM)… More >

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