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ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological

Anurodh Kumar1, Amit Vishwakarma1, Varun Bajaj2

  • 1PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.

Computer Methods and Programs in Biomedicine
|May 9, 2024
PubMed
Summary

This study introduces a novel deep learning model for accurate lung cancer classification from histopathological images. The proposed ML3CNet achieves high accuracy, aiding early detection and improving patient outcomes.

Keywords:
Histopathological imagesModel quantizationMulti-headed convolutional neural networkNon-local mean filterTransfer learning

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computational Pathology

Background:

  • Lung cancer (LC) poses a significant global health challenge with high mortality rates.
  • Early detection of LC is crucial for improving patient survival and enabling preventative measures.
  • Traditional histopathological diagnosis is time-consuming and subjective, necessitating advanced diagnostic tools.

Purpose of the Study:

  • To develop an effective convolutional neural network (CNN)-based architecture for classifying lung tissue subtypes using histopathological images.
  • To address the limitations of manual inspection and traditional machine learning methods in lung cancer diagnosis.

Main Methods:

  • Utilized a nonlocal mean (NLM) filter for noise reduction in histopathological images, preserving image edges.
  • Proposed a multi-headed lung cancer classification convolutional neural network (ML3CNet) for image analysis.
  • Implemented model quantization to reduce ML3CNet's size for efficient storage and faster processing.

Main Results:

  • The ML3CNet model achieved high performance metrics, including 99.72% average classification accuracy, 99.66% sensitivity, and 99.64% precision.
  • A quantized version of the model attained 98.92% accuracy.
  • The model's applicability was validated on a colon cancer dataset.

Conclusions:

  • The proposed approach offers a precise, automated method for classifying lung cancer subtypes, supporting clinical decision-making.
  • The ML3CNet model demonstrates potential for practical implementation on hardware like Raspberry Pi for real-world applications.