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Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.

التفاصيل البيبلوغرافية
العنوان: Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.
المؤلفون: Faruqui N; Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh.; Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh., Yousuf MA; Institute of Information Technology (IIT), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh., Kateb FA; Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia., Abdul Hamid M; Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia., Monowar MM; Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
المصدر: Heliyon [Heliyon] 2023 Oct 27; Vol. 9 (11), pp. e21520. Date of Electronic Publication: 2023 Oct 27 (Print Publication: 2023).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101672560 Publication Model: eCollection Cited Medium: Print ISSN: 2405-8440 (Print) Linking ISSN: 24058440 NLM ISO Abbreviation: Heliyon Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: London : Elsevier Ltd, [2015]-
مستخلص: The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered by Convolutional Neural Network (CNN)-based classifiers. Critical areas of study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial to improve the technology's usability-a factor often neglected in current state-of-the-art research. Yet, current state-of-the-art research in this field frequently overlooks the need for expediting this process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired by Software-As-A-Service (SAAS) within the cloud computing paradigm. As a comprehensive lung cancer diagnosis service system, HAAS has the potential to reduce lung cancer mortality rates by providing early diagnosis opportunities to everyone. We present HAASNet, a cloud-compatible CNN that boasts an accuracy rate of 96.07%. By integrating HAASNet predictions with physio-symptomatic data from the Internet of Medical Things (IoMT), the proposed HAAS model generates accurate and reliable lung cancer diagnosis reports. Leveraging IoMT and cloud technology, the proposed service is globally accessible via the Internet, transcending geographic boundaries. This groundbreaking lung cancer diagnosis service achieves average precision, recall, and F1-scores of 96.47%, 95.39%, and 94.81%, respectively.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 The Author(s).)
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فهرسة مساهمة: Keywords: CNN; CT; Cloud computing; Computer tomography images; Convolutional neural network; Internet of medical things; IoMT; Lung cancer classification; Optimization algorithms
تواريخ الأحداث: Date Created: 20231109 Latest Revision: 20231110
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC10628703
DOI: 10.1016/j.heliyon.2023.e21520
PMID: 37942151
قاعدة البيانات: MEDLINE
الوصف
تدمد:2405-8440
DOI:10.1016/j.heliyon.2023.e21520