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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional

Nasr Y Gharaibeh1, Roberto De Fazio2, Bassam Al-Naami3

  • 1Department of Electrical Engineering, Al-Balqa Applied University, Salt 21163, Jordan.

Journal of Imaging
|July 26, 2024
PubMed
Summary

This study introduces an AI-driven approach for lung cancer diagnosis using CT scans. The method accurately classifies images as benign, normal, or malignant, improving diagnostic efficiency.

Keywords:
AIButterworth smooth filterChaotic Crow Search Algorithm and Random Forest (CCSA-RF)Multi-space Image Reconstruction (MIR) with Grey Level Co-occurrence Matrix (GLCM)Sparse Convolutional Neural Network (SCNN)lung cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Accurate lung cancer diagnosis and prognosis are vital for effective treatment planning.
  • Computed tomography (CT) scans are increasingly used in pathology, necessitating advanced image analysis.
  • Artificial intelligence (AI), particularly deep learning, shows significant potential in analyzing pathology images for tasks like tumor detection and characterization.

Purpose of the Study:

  • To propose an AI-based methodology for accurate lung cancer diagnosis using CT images.
  • To develop a hybrid approach combining image processing, feature selection, and deep learning for lung cancer classification.
  • To evaluate the performance of the proposed AI model in distinguishing between benign, normal, and malignant lung tissues.

Main Methods:

  • Utilized the LUNA 16 lung cancer dataset for image analysis.
  • Applied Butterworth smooth filter for noise reduction and Chaotic Crow Search Algorithm with Random Forest (CCSA-RF) for feature selection.
  • Employed Multi-space Image Reconstruction (MIR) with Grey Level Co-occurrence Matrix (GLCM) for feature extraction.
  • Implemented Lung Tumor Severity Classification (LTSC) using Sparse Convolutional Neural Network (SCNN) and Probabilistic Neural Network (PNN) for classification.

Main Results:

  • The developed AI methodology successfully filtered noise and selected relevant features (diameter, margin, spiculation, lobulation, subtlety, malignancy).
  • The hybrid model achieved efficient classification of lung cancer images into benign, normal, and malignant categories using PNN.
  • Performance metrics including accuracy, precision, F-score, sensitivity, and specificity were calculated to validate the model's effectiveness.

Conclusions:

  • The proposed AI-based methodology offers an efficient and accurate approach for lung cancer diagnosis from CT scans.
  • The hybrid model integrating CCSA-RF, MIR-GLCM, SCNN, and PNN demonstrates strong potential in computer-assisted diagnosis of lung cancer.
  • Further evaluation and comparison with existing methods confirm the effectiveness of this novel AI solution in improving diagnostic outcomes.