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

2025-12-18

AI Summary

To address the high error rates and time consumption associated with manual interpretation of microscopic blood images in acute lymphoblastic leukemia (ALL) diagnosis, this paper proposes a lightweight, IoT-enabled automatic classification method for medical Internet-of-Medical-Things (IoMT) systems. The method synergistically integrates convolutional neural networks (CNNs) for multi-scale spatial feature extraction with higher-order singular value decomposition (HOSVD) as an interpretable and robust backend classifier, enabling effective feature compression and dimensionality reduction to enhance edge-device deployment efficiency. Notably, this is the first work to embed HOSVD within an IoMT end–edge–cloud collaborative architecture, supporting real-time clinician–patient interactive diagnosis. Evaluated on the ALL-IDB2 benchmark dataset, the system achieves 98.88% classification accuracy—surpassing conventional approaches—while ensuring high precision, low inference latency, and clinical deployability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Segmentation
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Automates leukemia classification using CNN and HOSVD
• Addresses manual diagnosis errors and time inefficiencies
• Enables real-time IoMT-based detection from blood images

Innovation

• CNN for leukemia cell classification
• HOSVD as novel classifier in medical imaging
• IoMT enables real-time patient-clinician communication