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Unified framework model for detecting and organizing medical cancerous images in IoMT systems

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Abstract

One of the challenges that arise when utilizing real-time reaction services, such as constructing deep learning models within the Internet of Medical Things (IoMT) infrastructure, is effectively balancing the computation load between the cloud and fog computing layers. This paper proposes a unified framework of offline training and online response to the healthcare professional. The framework gathers medical images from various heterogeneous IoMT devices and then arranges them into homogeneous locations in the cloud, using a stage-one classification stage (or offline training). Furthermore, the stage-two classification (or online response) is employed to detect the type of cancer for each homogeneous location containing the same image type within the cloud. To evaluate the framework, we conducted extensive experiments on six well-known cancer datasets of multiple types. The stage-one classification shows superior results of the error rates for the InceptionResNetV2 and DenseNet201 pre-trained transfer learning models of 0.33% and 0.43% with accuracy values of 99.67% and 99.57% respectively. In the stage-two classification, the results show different performances on each dataset. The point is that each dataset is organized separately which helps in studying the influence of pre-trained transfer learning models and improving their performance in the absence of intervention and bias in datasets.

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Authors and Affiliations

  1. Department of Computer Information Systems The University of Jordan, Aqaba, 77110, Jordan

    Rami S. Alkhawaldeh

  2. Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates

    Saja Al-Dabet

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  1. Rami S. Alkhawaldeh
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Contributions

Rami S. Alkhawaldeh and Saja Al-Dabet: Conceptualization; Rami S. Alkhawaldeh and Saja Al-Dabet: Data curation; Rami S. Alkhawaldeh: Formal analysis; Rami S. Alkhawaldeh and Saja Al-Dabet: Funding acquisition; Saja Al-Dabet: Investigation; Rami S. Alkhawaldeh and Saja Al-Dabet: Methodology; Rami S. Alkhawaldeh: Project administration; Rami S. Alkhawaldeh and Saja Al-Dabet: Resources; Rami S. Alkhawaldeh and Saja Al-Dabet: Software; Supervision; Rami S. Alkhawaldeh and Saja Al-Dabet: Validation; Rami S. Alkhawaldeh: Visualization; Rami S. Alkhawaldeh and Saja Al-Dabet: Roles/Writing - original draft; Saja Al-Dabet: Writing - review & editing

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Correspondence to Rami S. Alkhawaldeh.

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Alkhawaldeh, R.S., Al-Dabet, S. Unified framework model for detecting and organizing medical cancerous images in IoMT systems. Multimed Tools Appl 83, 37743–37770 (2024). https://doi.org/10.1007/s11042-023-16883-9

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