Healthcare AI
IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value Decomposition
This research introduces an IoMT-based framework for rapid and accurate Acute Lymphocytic Leukemia (ALL) diagnosis. By combining a Convolutional Neural Network (CNN) for feature extraction with a novel Higher-Order Singular Value Decomposition (HOSVD) classifier, the system aims to overcome limitations of manual examination, such as human error and time consumption. It is designed to facilitate real-time communication between patients and clinicians, significantly improving early diagnosis and treatment planning for this critical hematic disease. The model achieved 98.88% accuracy on the ALL-IDB2 dataset.
Executive Impact at a Glance
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Accuracy Achieved
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Deep Analysis & Enterprise Applications
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The paper introduces Internet of Medical Things (IoMT) as a critical application of IoT in healthcare, enabling high-speed medical device connectivity for data collection and transfer. It highlights the importance of Acute Lymphocytic Leukemia (ALL), a rapidly spreading hematic cancer, where early and accurate diagnosis is crucial. Traditional manual methods by pathologists are prone to human error and are time-consuming, necessitating AI-based solutions like Deep Neural Networks (DNNs) for identifying cancer from non-cancer tissue. CNNs are recognized as highly efficient ML methods for feature extraction and image classification due to their multi-layered architecture.
The core of the proposed method involves a Convolutional Neural Network (CNN) for extracting effective features from microscopic blood images, combined with a Higher-Order Singular Value Decomposition (HOSVD) as a novel classifier. CNN's ability to extract abstract and invariant features from various image data parts makes it suitable for leukemia classification. HOSVD, a multi-linear algebra method, offers faster training speeds, fewer tuning parameters, and higher accuracy compared to traditional DL classifiers. The entire framework is designed to integrate into an IoMT environment, allowing real-time communication between hematologists and patients for rapid and safe leukemia classification.
The model was implemented on the ALL-IDB2 database, achieving an average accuracy of 98.88% with a standard deviation of 0.009. Comparative analysis against other state-of-the-art classifiers (CNN, SVM, ELM, KNN) showed HOSVD outperformed them in both accuracy and standard deviation. This high accuracy, combined with its integration into the IoMT framework, positions the proposed method as a robust solution for automated leukemia diagnosis, overcoming limitations of manual approaches and enhancing real-time medical decision-making.
98.88%
Average Accuracy Achieved
Enterprise Process Flow
Blood Sample Collection
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Wireless Digital Microscope Scan
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Cloud Medical Server Upload
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CNN Feature Extraction
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HOSVD Classification
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Real-time Diagnosis to Clinician & Patient
| Classifier | Average Accuracy (%) | Standard Deviation |
|---|---|---|
| CNN (Baseline) | 97.75 | 0.015 |
| SVM | 98.12 | 0.0174 |
| ELM | 97.75 | 0.0154 |
| KNN | 98.10 | 0.0175 |
| Proposed CNN-HOSVD | 98.88 | 0.009 |
Real-world Impact: Early Leukemia Detection
Imagine a pediatric clinic utilizing this IoMT-based system. A routine blood test for a child with vague symptoms is quickly processed. The wireless digital microscope captures images, which are immediately sent to the cloud server. The CNN-HOSVD model rapidly identifies abnormal cells, flagging potential ALL within minutes, compared to hours or days for manual review. This early alert allows specialists to intervene sooner, drastically improving the child’s prognosis. The system’s 98.88% accuracy minimizes false positives and negatives, building clinician trust and enabling swift, decisive action. This IoMT integration transforms a lengthy diagnostic process into a real-time, life-saving intervention.
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Your AI Implementation Roadmap
A structured approach to integrating IoMT-based AI for leukemia diagnosis into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Data Integration
Assess existing diagnostic workflows, data sources (e.g., LIMS, PACS), and infrastructure. Plan secure data pipelines for image transfer and integration with IoMT devices. Define success metrics and initial deployment scope.
Phase 2: Model Customization & Training
Leverage ALL-IDB2 and client-specific historical data to fine-tune CNN-HOSVD models. Establish robust validation protocols. Conduct iterative training to optimize accuracy for unique operational contexts.
Phase 3: IoMT Deployment & Secure Cloud Integration
Deploy wireless digital microscopes and edge devices. Configure secure cloud infrastructure for real-time data processing and model inference. Ensure HIPAA/GDPR compliance and robust cybersecurity measures.
Phase 4: Pilot Program & User Training
Run a pilot program with a subset of diagnostic staff to gather feedback. Train pathologists and technicians on new workflows and AI interface. Refine the system based on real-world usage.
Phase 5: Full Rollout & Continuous Optimization
Scale the solution across the enterprise. Implement continuous monitoring of model performance and data quality. Establish processes for regular model updates and adaptive learning to maintain peak efficiency.
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