Aya M. Al-Zoghby^1,2, Ahmed Ismail Ebada^1,*, Aya S. Saleh¹, Mohammed Abdelhay³, Wael A. Awad¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4155-4193, 2025, DOI:10.32604/cmc.2025.065571 - 30 July 2025

Abstract Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics, advancing precision medicine by enabling integration and learning from diverse data sources. The exponential growth of high-dimensional healthcare data, encompassing genomic, transcriptomic, and other omics profiles, as well as radiological imaging and histopathological slides, makes this approach increasingly important because, when examined separately, these data sources only offer a fragmented picture of intricate disease processes. Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling, more robust disease characterization, and improved treatment decision-making. This review… More >

Tony Thomas^*, Ravi Prakash, Soumya Pal

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025

Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >

Sameera V Mohd Sagheer^1,*, Meghana K H², P M Ameer³, Muneer Parayangat⁴, Mohamed Abbas⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4259-4297, 2025, DOI:10.32604/cmc.2025.063726 - 30 July 2025

Abstract Integrating multiple medical imaging techniques, including Magnetic Resonance Imaging (MRI), Computed Tomography, Positron Emission Tomography (PET), and ultrasound, provides a comprehensive view of the patient health status. Each of these methods contributes unique diagnostic insights, enhancing the overall assessment of patient condition. Nevertheless, the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution, data collection methods, and noise levels. While traditional models like Convolutional Neural Networks (CNNs) excel in single-modality tasks, they struggle to handle multi-modal complexities, lacking the capacity to model global relationships. This research presents a novel approach for… More >

Subrata Kumer Paul^1,2, Abu Saleh Musa Miah^3,4, Rakhi Rani Paul^1,2, Md. Ekramul Hamid², Jungpil Shin^4,*, Md Abdur Rahim⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025

Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >

Jitendra Kumar Samriya¹, Virendra Singh², Gourav Bathla³, Meena Malik⁴, Varsha Arya^5,6, Wadee Alhalabi⁷, Brij B. Gupta^8,9,10,11,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025

Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >

Noor Ayesha¹, Muhammad Mujahid², Abeer Rashad Mirdad², Faten S. Alamri^3,*, Amjad R. Khan²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1883-1899, 2025, DOI:10.32604/cmc.2025.063560 - 09 June 2025

Abstract Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an… More >

Yogendra P. S. Maravi^*, Nishchol Mishra

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5517-5537, 2025, DOI:10.32604/cmc.2025.063964 - 19 May 2025

Abstract The growing demand for international travel has highlighted the critical need for reliable tools to verify travelers’ healthcare status and meet entry requirements. Personal health passports, while essential, face significant challenges related to data silos, privacy protection, and forgery risks in global sharing. To address these issues, this study proposes a blockchain-based solution designed for the secure storage, sharing, and verification of personal health passports. This innovative approach combines on-chain and off-chain storage, leveraging searchable encryption to enhance data security and optimize blockchain storage efficiency. By reducing the storage burden on the blockchain, the system… More >

Veena Dillshad¹, Muhammad Attique Khan^2,*, Muhammad Nazir¹, Jawad Ahmad², Dina Abdulaziz AlHammadi³, Taha Houda², Hee-Chan Cho⁴, Byoungchol Chang^5,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5299-5321, 2025, DOI:10.32604/cmc.2025.063851 - 19 May 2025

Abstract Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer… More >

Ahmed Ben Atitallah^1,*, Jannet Kamoun^2,3, Meshari D. Alanazi¹, Turki M. Alanazi⁴, Mohammed Albekairi¹, Khaled Kaaniche¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5761-5779, 2025, DOI:10.32604/cmc.2025.063634 - 19 May 2025

Abstract Breast Cancer (BC) remains a leading malignancy among women, resulting in high mortality rates. Early and accurate detection is crucial for improving patient outcomes. Traditional diagnostic tools, while effective, have limitations that reduce their accessibility and accuracy. This study investigates the use of Convolutional Neural Networks (CNNs) to enhance the diagnostic process of BC histopathology. Utilizing the BreakHis dataset, which contains thousands of histopathological images, we developed a CNN model designed to improve the speed and accuracy of image analysis. Our CNN architecture was designed with multiple convolutional layers, max-pooling layers, and a fully connected… More >

Shrabani Sutradhar^1,2, Rajesh Bose³, Sudipta Majumder¹, Arfat Ahmad Khan^4,*, Sandip Roy³, Fasee Ullah⁵, Deepak Prashar^6,7

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3975-4029, 2025, DOI:10.32604/cmc.2025.061965 - 19 May 2025

Abstract Cloud-based setups are intertwined with the Internet of Things and advanced, and technologies such as blockchain revolutionize conventional healthcare infrastructure. This digitization has major advantages, mainly enhancing the security barriers of the green tree infrastructure. In this study, we conducted a systematic review of over 150 articles that focused exclusively on blockchain-based healthcare systems, security vulnerabilities, cyberattacks, and system limitations. In addition, we considered several solutions proposed by thousands of researchers worldwide. Our results mostly delineate sustained threats and security concerns in blockchain-based medical health infrastructures for data management, transmission, and processing. Here, we describe… More >