IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value

• URL: http://arxiv.org/abs/2512.16448v1
• Date: Thu, 18 Dec 2025 12:09:45 GMT
• Title: IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value
• Authors: Shabnam Bagheri Marzijarani, Mohammad Zolfaghari, Hedieh Sajedi,
• Abstract summary: The Internet of Things (IoT) is a concept by which objects find identity and can communicate with each other in a network.<n>One of the applications of the IoT is in the field of medicine, which is called the Internet of Medical Things (IoMT)
• Score: 3.1478972434597186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet of Things (IoT) is a concept by which objects find identity and can communicate with each other in a network. One of the applications of the IoT is in the field of medicine, which is called the Internet of Medical Things (IoMT). Acute Lymphocytic Leukemia (ALL) is a type of cancer categorized as a hematic disease. It usually begins in the bone marrow due to the overproduction of immature White Blood Cells (WBCs or leukocytes). Since it has a high rate of spread to other body organs, it is a fatal disease if not diagnosed and treated early. Therefore, for identifying cancerous (ALL) cells in medical diagnostic laboratories, blood, as well as bone marrow smears, are taken by pathologists. However, manual examinations face limitations due to human error risk and time-consuming procedures. So, to tackle the mentioned issues, methods based on Artificial Intelligence (AI), capable of identifying cancer from non-cancer tissue, seem vital. Deep Neural Networks (DNNs) are the most efficient machine learning (ML) methods. These techniques employ multiple layers to extract higher-level features from the raw input. In this paper, a Convolutional Neural Network (CNN) is applied along with a new type of classifier, Higher Order Singular Value Decomposition (HOSVD), to categorize ALL and normal (healthy) cells from microscopic blood images. We employed the model on IoMT structure to identify leukemia quickly and safely. With the help of this new leukemia classification framework, patients and clinicians can have real-time communication. The model was implemented on the Acute Lymphoblastic Leukemia Image Database (ALL-IDB2) and achieved an average accuracy of %98.88 in the test step.

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