A multi-model unified disease diagnosis framework for cyber healthcare using IoMT- cloud computing networks
Kamal Upretikamalupreti1989@gmail.comResearch FellowDepartment of Creative Technologies and Product DesignNational Taipei University of BusinessTaipei City, Taiwan, R.O.C.View full profile → , Sheng-Lung Pengslpeng@ntub.edu.twDepartment of Creative Technologies and Product DesignNational Taipei University of BusinessTaipei City, Taiwan, R.O.C.View full profile → , Pravin Ramdas Kshirsagarpravinrk88@yahoo.comDepartment of Data ScienceTulsiramji Gaikwad Patil College of Engineering & TechnologyNagpur, IndiaView full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and EngineeringSir Padampat Singhania UniversityUdaipur, Rajasthan, India0000-0001-8062-4144View full profile → , *Halah A. Al-AlshaikhCorresponding authorhamshaikh@imamu.edu.saDepartment of Information SystemsCollege of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University RiyadhSaudi ArabiaView full profile → , A. K. Sharmadr.arvindkumarsharma@gmail.comDepartment of Creative Technologies and Product DesignNational Taipei University of BusinessTaipei City, Taiwan, R.O.C.View full profile → , Ramesh Chandra Pooniarameshcpoonia@gmail.comDepartment of Computer ScienceCHRIST (Deemed to be University), Delhi-NCRGhaziabad, Uttar Pradesh, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 30 May 2023
- Published Online:
- 30 Sep 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-1831
- Pages:
- 1819–1834
Abstract
Keywords
Subject Classifications
References
[1] Y. Yin, Y. Zeng, X. Chen, and Y. Fan, The Internet of Things in healthcare: An overview,’’ J. Ind. Inf. Integr., vol. 1, pp. 3–13, Mar. (2016).
[2] M. Alhussein, G. Muhammad, M. S. Hossain, and S. U. Amin, Cognitive IoT-cloud integration for smart healthcare: Case study for epileptic seizure detection and monitoring, Mobile Netw. Appl., vol. 23, no. 6, pp. 1624–1635, Dec. (2018).
[3] Pachiyannan, Prabu, Musleh Alsulami, Deafallah Alsadie, Abdul Khader Jilani Saudagar, Mohammed AlKhathami, and Ramesh Chandra Poonia. 2023. “A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease” Diagnostics 13, no. 13: 2195.
[4] A. Sekhar, S. Biswas, R. Hazra, A. K. Sunaniya, A. Mukherjee and L. Yang, Brain Tumor Classification Using Fine-Tuned GoogLeNet Features and Machine Learning Algorithms: IoMT Enabled CAD System, in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 3, pp. 983-991, March (2022)
[5] Nancy, A. Angel, et al. IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning. Electronics vol. 11, no. 15 pp. 2292, (2022).
[6] Haq, Amin UI, et al. IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare. IEEE Journal of Biomedical and Health Informatics (2022).
[7] Ihnaini, Baha, et al. A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Computational Intelligence and Neuroscience 2021 (2021).
[8] A. Windmon et al., TussisWatch: A Smart-Phone System to Identify Cough Episodes as Early Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure, in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1566-1573, July (2019).
[9] S. Huda, J. Yearwood, H. F. Jelinek, M. M. Hassan, G. Fortino and M. Buckland, A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis, in IEEE Access, vol. 4, pp. 9145-9154, (2016).
[10] Najib A. Kofahi, Mohammad Nizar Mesmar & S. H. Gharaibeh (2002) A computer algorithm for the evaluation of heavy metal toxicity in fresh water habitats, Journal of Interdisciplinary Mathematics, 5:1, 85-96.
[11] National Health Portal of India, https://www.nhp.gov.in/disease-a-z.
[12] Ruchi Sharma, Charu Arora, Arvind Rehalia & Anil Bhardwaj. Fruitfly optimizer with deep neural network for the detection of brain tumours using EEG signals, Journal of Information and Optimization Sciences, 43:1, 63-70 (2022),
[13] Figshare brain tumor dataset, Accessed: Feb. (2021). [Online]. Available: https://do.org/10.6084/-m9.figshare.1512427.v5.
[14] https://www.kaggle.com/madhucharan/alzheimersdisease5classdatasetadni .
[15] Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S, Thoma G, Wang YX, Lu PX, McDonald CJ., Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging, vol. 33, no. 2, pp. 233-45, (2014).
[16] M. Zak and A. Krzyżak, Classification of Lung Diseases Using Deep Learning Models,” Comput. Sci. – ICCS 2020, vol. 12139, pp. 621, (2020).
[17] R. Hooda, A. Mittal, and S. Sofat, Automated TB classification using ensemble of deep architectures, Multimed. Tools Appl. 2019 7822, vol. 78, no. 22, pp. 31515–31532, Jul. (2019).
[18] M. Z. Islam, M. M. Islam, and A. Asraf, A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, Informatics Med. Unlocked, vol. 20, pp. 100412, Jan. (2020).
[19] S. Deepak and P. M. Ameer, Brain tumor classification using deep CNN features via transfer learning, Comput. Biol. Med., vol. 111, pp. 103345, Aug. (2019).
[20] P. Afshar, K. N. Plataniotis, and A. Mohammadi, Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries, ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2019-May, pp. 1368–1372, May (2019).
[21] S. Qiu et al., “Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification,” Brain, vol. 143, no. 6, pp. 1920–1933, Jun. (2020).
[22] F. Li and M. Liu, Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks, Comput. Med. Imaging Graph., vol. 70, pp. 101–110, Dec. (2018).
[23] W. Zhu, L. Sun, J. Huang, L. Han, and D. Zhang, Dual Attention Multi-Instance Deep Learning for Alzheimer’s Disease Diagnosis with Structural MRI, IEEE Trans. Med. Imaging, vol. 40, no. 9, pp. 2354–2366, Sep. (2021).
