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FP-CNN: Fuzzy pooling-based convolutional neural network for lung ultrasound image classification with explainable

Md Mahmodul Hasan1, Muhammad Minoar Hossain2, Mohammad Motiur Rahman1

  • 1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Dhaka, Bangladesh.

Computers in Biology and Medicine
|September 7, 2023
PubMed
Summary

This study introduces a fuzzy pooling-based convolutional neural network (FP-CNN) for classifying lung ultrasound images. The AI model accurately identifies COVID-19, pneumonia, and normal cases, aiding in rapid diagnosis.

Keywords:
COVID-19 diagnosisExplainable artificial intelligence (XAI)Fuzzy poolingUltrasound image classification

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Computer-Aided Diagnosis
  • Medical Image Analysis

Background:

  • The COVID-19 pandemic highlighted the need for efficient diagnostic tools.
  • Non-invasive ultrasound imaging shows promise as a biomarker for respiratory conditions.
  • Accurate classification of lung ultrasound images is crucial for timely medical diagnosis.

Purpose of the Study:

  • To develop an intelligent methodology for classifying lung ultrasound images.
  • To utilize a fuzzy pooling-based convolutional neural network (FP-CNN) for improved feature representation.
  • To enhance diagnostic decision transparency using explainable AI (SHAP).

Main Methods:

  • Development of a fuzzy pooling-based convolutional neural network (FP-CNN) for image classification.
  • Implementation of Shapley Additive Explanation (SHAP) for model interpretability.
  • Evaluation of various CNN architectures and fuzzy pooling strategies, including fine-tuning and multi-layer fuzzy pooling.

Main Results:

  • The FP-CNN model achieved classification of ultrasound images into COVID-19, normal, and pneumonia categories.
  • The Xception model, incorporating fuzzy pooling in all layers, demonstrated the highest accuracy at 97.2%.
  • SHAP analysis provided explanations for the FP-CNN model's diagnostic predictions.

Conclusions:

  • The proposed FP-CNN methodology offers a robust approach for diagnosing COVID-19 from lung ultrasound images.
  • The integration of fuzzy pooling enhances feature extraction for better classification accuracy.
  • Explainable AI methods like SHAP are vital for ensuring the trustworthiness of AI diagnostic systems.