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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Lung cancer histopathological image classification using wavelets and AlexNet.

Prabira Kumar Sethy1, A Geetha Devi2, Bikash Padhan1

  • 1Department of Electronics, Sambalpur University, Jyoti Vihar, Burla, India.

Journal of X-Ray Science and Technology
|December 4, 2022
PubMed
Summary

This study introduces a hybrid AI network for classifying lung histopathology images. The novel approach accurately distinguishes between cancerous and benign lung cells, improving diagnostic potential.

Keywords:
AlexNetLung cancercancer diagnosishistopathological imagessupport vector machine (SVM)wavelet

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Area of Science:

  • Digital pathology
  • Machine learning in oncology
  • Cancer image analysis

Background:

  • Lung cancer is a leading cause of cancer mortality globally.
  • Accurate histopathological classification is crucial for effective lung cancer treatment.
  • Distinguishing between lung adenocarcinoma, squamous cell carcinoma, and benign cells requires expert pathologist review.

Purpose of the Study:

  • To develop a hybrid artificial intelligence (AI) network for categorizing lung histopathology images.
  • To enhance the accuracy and efficiency of lung cancer subtype classification.
  • To assist pathologists in identifying cancerous and benign lung cells.

Main Methods:

  • A hybrid network combining AlexNet, wavelet transform, and support vector machines (SVMs) was developed.
  • Discrete wavelet transform (DWT) coefficients and AlexNet deep features were integrated.
  • The model was trained and tested on the LC25000 dataset containing 5,000 histopathology images.

Main Results:

  • The hybrid network achieved a classification accuracy of 99.3%.
  • An Area Under the Curve (AUC) of 0.99 was obtained, indicating high diagnostic performance.
  • The system effectively classified benign, adenocarcinoma, and squamous carcinoma lung cells.

Conclusions:

  • The developed hybrid AI network demonstrates high accuracy in classifying lung histopathology images.
  • This AI-driven approach shows significant potential for improving lung cancer diagnosis.
  • The integration of deep learning and wavelet features offers a promising direction for computational pathology.