Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation.

Publication date: Mar 15, 2024

Chest radiography is an essential diagnostic tool for respiratory diseases such as COVID-19, pneumonia, and tuberculosis because it accurately depicts the structures of the chest. However, accurate detection of these diseases from radiographs is a complex task that requires the availability of medical imaging equipment and trained personnel. Conventional deep learning models offer a viable automated solution for this task. However, the high complexity of these models often poses a significant obstacle to their practical deployment within automated medical applications, including mobile apps, web apps, and cloud-based platforms. This study addresses and resolves this dilemma by reducing the complexity of neural networks using knowledge distillation techniques (KDT). The proposed technique trains a neural network on an extensive collection of chest X-ray images and propagates the knowledge to a smaller network capable of real-time detection. To create a comprehensive dataset, we have integrated three popular chest radiograph datasets with chest radiographs for COVID-19, pneumonia, and tuberculosis. Our experiments show that this knowledge distillation approach outperforms conventional deep learning methods in terms of computational complexity and performance for real-time respiratory disease detection. Specifically, our system achieves an impressive average accuracy of 0. 97, precision of 0. 94, and recall of 0. 97.

Concepts Keywords
Accurate Chest X-ray
Apps Deep learning
Pneumonia Knowledge distillation techniques
Radiography Medical imaging
Trains Neural networks
Respiratory disease

Semantics

Type Source Name
disease MESH COVID-19
disease MESH pneumonia
disease MESH tuberculosis
pathway KEGG Tuberculosis
disease MESH respiratory diseases
disease VO viable
disease VO time

Original Article

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