In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays
This work addresses rapid COVID-19 diagnosis via X-rays, but it is incremental as it builds on existing methods for data augmentation and classification.
The study tackled the problem of imbalanced and limited COVID-19 lung chest X-ray data by applying image transformations to balance datasets, achieving 94% accuracy in multiclass classification using a simple CNN.
Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to indirectly aid in infection containment, however generating a reliably labeled image dataset for a novel disease is not an easy feat, nor is it of highest priority when combating a global pandemic. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states for other lung pathologies; this study aims to leverage that body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Furthermore, this study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.