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A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT.

Koyel Datta Gupta1, Deepak Kumar Sharma2, Shakib Ahmed2

  • 1Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India.

Neural Processing Letters
|June 14, 2021
PubMed
Summary

Timely disease detection is crucial for saving lives. This study introduces ReducedFireNet, a lightweight deep learning model for accurate, real-time histopathological image analysis, ideal for Internet of Medical Things devices.

Keywords:
Deep learningDisease diagnosisHistopathologicalImage classificationIoMT

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Area of Science:

  • Medical technology
  • Artificial intelligence
  • Digital pathology

Background:

  • Delayed disease diagnosis and treatment lead to preventable deaths globally.
  • Internet of Medical Things (IoMT) offers potential for real-time disease identification and patient care.
  • Current deep learning models for image analysis are often too large for IoMT devices.

Purpose of the Study:

  • To design a lightweight deep learning model for accurate histopathological image analysis.
  • To enable real-time disease identification using IoMT-enabled imaging devices.
  • To address the limitations of large, resource-intensive deep learning models in medical imaging.

Main Methods:

  • Development of a novel, lightweight deep learning model named ReducedFireNet.
  • Auto-classification of histopathological images using the proposed model.
  • Evaluation on an actual histopathological image dataset.

Main Results:

  • Achieved a mean accuracy of 96.88% and an F1 score of 0.968.
  • The ReducedFireNet model is highly efficient, with a size of only 0.391 MB.
  • Demonstrated a low computational requirement of 0.201 GFLOPS.

Conclusions:

  • ReducedFireNet is a highly accurate and efficient model for histopathological image analysis.
  • Its lightweight design makes it suitable for deployment on IoMT imaging equipment.
  • The model shows significant promise for improving early disease detection and patient outcomes.