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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Effective multi-class lungdisease classification using the hybridfeature engineering mechanism.

Binju Saju¹, Neethu Tressa¹, Rajesh Kumar Dhanaraj²

¹Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India.

Mathematical Biosciences and Engineering : MBE

|December 5, 2023

Summary

This summary is machine-generated.

A new hybrid deep learning model accurately classifies 13 lung diseases from chest X-rays. This advanced computer-assisted diagnosis system achieves 97.00% accuracy, aiding medical professionals in rapid and precise lung disease detection.

Keywords:

Aquila optimizer Canny edge detection DENSENET121 batch equalization chest X-ray contrast limited adaptive histogram equalization lung disease otsu

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Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

The COVID-19 pandemic highlighted the critical need for advanced lung disease diagnosis.
Computational models are increasingly vital for medical image classification and supporting clinical decisions.
Accurate and swift diagnosis of diverse lung pathologies from chest radiographs remains a significant challenge.

Purpose of the Study:

To introduce an advanced computer-assisted model for classifying 13 distinct lung diseases using deep learning on chest X-ray images.
To enhance image quality and extract relevant features for improved diagnostic accuracy.
To propose and evaluate a novel hybrid deep learning model for lung disease classification.

Main Methods:

Utilized an open-source dataset of 112,000 chest X-ray images.
Applied preprocessing techniques including Otsu-based binary conversion, adaptive histogram equalization, and Canny edge detection for image enhancement.
Employed feature extraction methods (connected regions, HOG, GLCM, Haar wavelet) and selection (RCNA), proposing an optimized hybrid model combining Convolutional Neural Networks (CNN) and DENSENET121 with Aquila optimization and batch equalization.

Main Results:

The proposed hybrid model achieved superior performance compared to standalone CNN and DENSENET121.
Achieved an accuracy of 97.00%, precision of 94.00%, sensitivity of 96.00%, specificity of 96.00%, and an F1-score of 95.00% for classifying 13 lung diseases.
Demonstrated the effectiveness of the optimized hybrid approach in medical image classification for lung diseases.

Conclusions:

The developed optimized hybrid deep learning model significantly advances the capability for accurate lung disease classification from chest radiographs.
The model's high performance metrics indicate its potential as a valuable tool for medical professionals in diagnosing lung conditions.
Future work may involve integrating explainable AI and further model optimization for enhanced clinical utility.