Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia

Abstract

Purpose – This study focuses on the application of a specific subfield of artificial intelligence referred to as computer vision in the analysis of 2-dimensional lung x-ray images for the assisted medical diagnosis of ordinary pneumonia.

Method – A convolutional neural network algorithm was implemented in a Python-coded, Flask-based web application that can analyze x-ray images for the detection of ordinary pneumonia. Since convolutional neural network algorithms rely on machine learning for the identification and detection of patterns, a technique referred to as transfer learning was implemented to train the neural network in the identification and detection of patterns within the dataset. Open-source lung x-ray images were used as training data to create a knowledge base that served as the core element of the web application and the experimental design employed a 5-Trial Confirmatory Test for the validation of the web application.

Results –The tabulated results of the 5-Trial Confirmatory Test show the calculation of Diagnostic Precision Percentage per Trial, General Diagnostic Precision Percentage, and General Diagnostic Error Percentage while the Confusion Matrix further shows the relationship between the label and the corresponding diagnosis result of the web application on each test images.

Conclusion – The successful generation of the h5 knowledge base proved that the CNN algorithm implementation for machine learning can generate patterns based on analyzing open-source datasets. The successful implementation and deployment of the web application to the cloud server proved that such a system can be feasibly deployed in such a platform. The experimental data proved the high precision of the analysis, proved that it can be used in the diagnosis of ordinary pneumonia under the supervision of medical practitioners.

Recommendations – In retrospect, the precision of the diagnostic results could be enhanced further by utilizing a much larger training dataset for the machine learning phase. Since machine learning algorithms rely on the probability theory concept of the Law of Large Numbers, a large, high-quality dataset is crucial in yielding high-precision results. A more balanced training dataset is also necessary to avoid any kind of statistical bias during machine learning. A more balanced dataset could negate the use of data augmentation functionalities, thereby improving the efficiency and speed of the machine learning phase.

Practical Implications – The developed web application can be used by medical practitioners in A.I.-assisted diagnosis of ordinary pneumonia, and by researchers in the fields of computer science and bioinformatics.