Development and Validation of a Machine Learning System for Analysis and Radiological Diagnosis of Digital Chest X-ray Images
Keywords:Machine learning system, radiological diagnosis, digital chest X-ray.
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
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).