Development and Validation of a Machine Learning System for Analysis and Radiological Diagnosis of Digital Chest X-ray Images

Authors

  • Mehrunnissa Khanom Associate Professor Department of Medicine, Chattagram International Medical College, Chittagong
  • Aseef Iqbal Associate Professor, Department of Computer Science and Engineering, School of Science & Engineering, Chittagong Independent University, Chittagong
  • Afroza Hoque Assistant Professor, Department of Medical Education Unit, Chattagram International Medical College, Chittagong
  • Fatiha Tasmin Jeenia Associate Professor, Department of Pharmacology, Chattagram International Medical College, Chittagong
  • Muslim Uddin Professor, Department of Paediatrics, Chattagram International Medical College, Chittagong

DOI:

https://doi.org/10.3329/jom.v24i2.67273

Keywords:

Machine learning system, radiological diagnosis, digital chest X-ray.

Abstract

Introduction: Medicine is identified as one of the most promising field of application for Artificial Intelligence (AI). The current research presents development and validation of a machine learning system to diagnose chest x-ray images with higher accuracy.

Methodology: It was a multi-cantered, experimental study conducted from 01 July, 2021 to 30 June, 2022. The experiment was a two-step process; in the first step, a machine learning system (MLS) was developed through training, testing and tuning a specialised computer hardware & software utilising 5600 chest X ray images from NIH (National Institute of Health) chest X ray dataset. In the second step, 500 unseen chest X ray images from study centres were allowed to be diagnosed by the machine learning system and results were compared with expert opinions.

Result: After the system was developed, validation was done on 3 different variations of Deep Residual Network and tested for their accuracy in classifying the findings. Using ResNet50V2, an average accuracy of 84.37%% was achieved. With case-specific variation, highest accuracy was 94.84%, highest specificity was 97.23% and highest sensitivity was 88.25%.

Conclusion: With utilisation of this machine learning system, a faster radiological diagnosis of huge number of X ray images will become possible using only a small computer. Dependency on manpower, logistic support as well as rate of human-made errors can be minimised. However, this machine is never meant to replace an expert human opinion and it can never think beyond the box.

J MEDICINE 2023; 24(2): 112-118

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Published

2023-07-02

How to Cite

Khanom, M., Iqbal, A. ., Hoque, A., Jeenia, F. T. ., & Uddin, M. (2023). Development and Validation of a Machine Learning System for Analysis and Radiological Diagnosis of Digital Chest X-ray Images. Journal of Medicine, 24(2), 112–118. https://doi.org/10.3329/jom.v24i2.67273

Issue

Vol. 24 No. 2 (2023)

Section

Original Articles

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