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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

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Published on: May 19, 2023

Content-based medical image retrieval system for lung diseases using deep CNNs.

Shubham Agrawal¹, Aastha Chowdhary¹, Saurabh Agarwala¹

¹Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka 575025 India.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management

|July 6, 2022

Summary

This study introduces a content-based medical image retrieval (CBMIR) system for early lung disease detection using deep learning on X-ray images. The novel CBMIR approach significantly improved retrieval precision and classification accuracy for lung conditions.

Keywords:

COVID-19 Content-based image retrieval Deep learning Disease classification

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Content-based image retrieval (CBIR) systems analyze latent image features for relevance, bypassing textual descriptions.
Traditional CBIR relies on feature vectors for similarity matching.
Accurate retrieval of medical images is crucial for diagnostics.

Purpose of the Study:

To propose a content-based medical image retrieval (CBMIR) system for early lung disease detection and classification.
To leverage deep neural models and transfer learning for analyzing lung X-ray images.
To enhance the accuracy of identifying disease-specific features in medical imaging.

Main Methods:

Developed a CBMIR system utilizing deep neural networks.
Employed transfer learning on COVID-19 Chest X-ray datasets for model training.
Evaluated the system's performance on a standard medical image dataset.

Main Results:

Achieved a 49.71% improvement in precision across various distance metrics.
Observed a 26.55% improvement in the area under the precision-recall curve (AUPRC).
Demonstrated enhanced identification and classification of disease-specific features.

Conclusions:

The proposed CBMIR system shows significant potential for early lung disease detection.
Deep learning and transfer learning are effective for analyzing medical images in CBMIR.
The system offers a substantial improvement in retrieval and classification performance for lung X-rays.