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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Published on: December 19, 2020

MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model.

Ayesha Ahoor¹, Fahim Arif¹, Muhammad Zaheer Sajid¹

¹Department of Computer Software Engineering, MCS, National University of Science and Technology, Islamabad 44000, Pakistan.

Diagnostics (Basel, Switzerland)

|October 28, 2023

Summary

This summary is machine-generated.

This study introduces MixNet-LD, an automated system for classifying lung disease severity. The novel approach achieves 98.5% accuracy, improving medical image analysis for conditions like pneumonia and lung cancer.

Keywords:

COVID-19 deep learning (DL)deep neural networks dense blocks loss function pneumonia radiology tuberculosis

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Area of Science:

Medical Imaging and Artificial Intelligence
Computer-Aided Diagnosis
Respiratory Medicine

Background:

Lung diseases pose significant health risks, necessitating accurate severity assessment for effective management.
Current treatment focuses on controlling irreversible lung disease severity.
Automated, consistent methods are needed for reliable lung illness intensity determination.

Purpose of the Study:

To develop an automated approach, MixNet-LD, for identifying and categorizing lung disease severity.
To leverage an upgraded pre-trained MixNet model for enhanced lung illness classification.
To improve the accuracy and efficiency of medical image analysis in diagnosing lung conditions.

Main Methods:

Developed MixNet-LD, an automated system using a pre-trained MixNet model.
Implemented a pre-processing strategy with Grad-Cam for noise reduction and feature highlighting.
Utilized data augmentation for dataset balancing and employed dense blocks for improved classification.
Classified images into normal, COVID-19, pneumonia, tuberculosis, and lung cancer categories using an SVM classifier.

Main Results:

MixNet-LD achieved state-of-the-art performance with manageable model complexity.
The system demonstrated high accuracy, reaching 98.5% on a challenging lung disease dataset.
Tested on diverse datasets including the novel Pak-Lungs dataset.

Conclusions:

MixNet-LD effectively enhances classification accuracy in medical image analysis.
The proposed approach offers improved performance and learning capabilities for lung disease detection.
This research contributes to developing advanced medical image processing strategies for clinical applications.