What is it about?
Lung diseases pose significant challenges to public health worldwide, requiring accurate and efficient diagnostic methods for timely intervention. This research introduces a novel approach that uses deep learning algorithms on chest X-ray images generated from the fusion of individual datasets pertaining to Tuberculosis (TB), COVID-19, and Pneumonia to classify various lung illnesses. Employing the cutting-edge deep learning architectures such as MobileNetV2, Visual Geometry Group 16 (VGG16), InceptionNet, ResNet50, and EfficientNet, the usefulness of textural features derived using Local Binary Patterns (LBP) for improving classification performance is investigated. The EfficientNet and ResNet design achieves remarkable accuracy of 96.3% and 97.1% respectively on the fusion dataset. It is tested rigorously utilizing compound scaling to scale up the network in depth, width, and resolution simultaneously. This has resulted in better feature extraction and image representation. Additionally, methods for enhancing model performance are covered, such as feature engineering, data augmentation, and hyperparameter tuning.
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Photo by National Cancer Institute on Unsplash
Why is it important?
The findings demonstrate the potential of textural features-based deep learning techniques to effectively diagnose multiple lung diseases with subtle difference in classification results from original features of chest X-ray images. This research has a positive synergy in the clinical decision-making process and health care outcomes.
Perspectives
The findings highlight the potential of deep learning approaches with texture based feature engineering to improve healthcare diagnostic capacities, opening the door to more precise and effective illness diagnosis and prognosis. The results demonstrate the value of incorporating texture-based information alongside traditional image data.
Ilavarasi A K
Vellore Institute of Technology, Chennai
Read the Original
This page is a summary of: Transfer Learning Based Multi-Class Lung Disease Prediction Using Textural Features Derived From Fusion Data, IEEE Access, January 2024, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/access.2024.3435680.
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